نشریه اطلاعات جغرافیایی (سپهر)
پیاپی 105 (بهار 1397)

Wireless Sensor Networks (WSNs) are widely used for monitoring and observation of dynamic phenomena. A sensor in WSNs covers only a limited region, depending on its sensing and communicating ranges, as well as the environment configuration. For efficient deployment of sensors in a WSN, the coverage estimation is a critical issue.
Probabilistic methods are among the most accurate models proposed for sensor coverage estimation. However, most of these methods are based on raster representation of the environment for coverage estimation which limits their quality. In this paper, we propose a probabilistic method for estimation of the coverage of a sensor network based on raster models, and 3D vector representation of the environment. Then, the performance of global approaches are evaluated, and the 3D vector model is used as an appropriate model.
Recent advances in electro mechanical and communication technologies have resulted in the development of more efficient, low cost and multi-function sensors. These tiny and ingenious devices are usually deployed in a wireless network to monitor and collect physical and environmental information such as motion, temperature, humidity, pollutants, traffic flow, etc. The information is then communicated to a process center where they are integrated and analyzed for different application. Deploying sensor networks allows inaccessible areas to be covered by minimizing the sensing costs compared to the use of separate sensors to completely cover the same area. Sensors may be spread with various densities depending on the area of application and details and quality of the information required. Despite the advances in sensor network technology, the efficiency of a sensor network for collection and communication of the information may be constrained by the limitations of sensors deployed in the network nodes. These restrictions may include sensing range, battery power, connection ability, memory, and limited computation capabilities. These limitations have been addressed by many researchers in recent years from various disciplines in order to design and deploy more efficient sensor networks. Efficient sensor network deployment is one of the most important issues in sensor network filed that affects the coverage and communication between sensors in the network. Nodes use their sensing modules to detect events occurring in the region of interest. Each sensor is assumed to have a sensing range, which may be constrained by the phenomenon being sensed and the environment conditions. Hence, obstacles and environmental conditions affect network coverage and may result in holes in the sensing area. Communication between nodes is also important.
Information collected from the region should be transferred to a processing center, directly or via its adjacent sensor. In the latter case, each sensor needs to be aware of the position of other adjacent sensors in their proximity. In recent years, Wireless Sensor Networks (WSN) has been studied in several applications such as monitoring and
control different criteria from smart cities and intelligent transportation to land use planning and environmental monitoring. Sensor deployment for achieving the maximum coverage is one of the important issues in WSN. Hence, several optimization algorithms to achieve maximum coverage are used in the majority of researches.
Discussion and In a general classification, optimization algorithms for the sensor deployment with the aim of increasing coverage, are divided into local and global optimization algorithms. The feature of global algorithms is their randomness based on an evolutionary process. In all of these algorithms, the calculation of the sensor network coverage is essential as a target function. In fact, coverage improvement is done according to the coverage calculation method. In the previous researches, a simple model was considered as the environmental model for network sensors. In this research, raster and vector modeling in 2 and 3-dimensional spaces and the optimization algorithms of global performance for optimizing
the sensor layouts were compared evaluated. errorIn this study, two-dimensional and three-dimensional vector models were used as a precise environmental model. Most of the models in the previous studies considered the coverage to be binary (i.e. a point is covered by a sensor or not). For realistic modeling, this study considers the coverage as an issue, which means that the amount of coverage obtained based on parameters such as distance and angle of the sensor is expressed as a percentage between zero and one hundred. errorIn fact, all sensors are not sensed in the same way and will vary according to their various parameters. Since the purpose of this study is to compare the performance and ability of global optimization algorithms, it is therefore assumed that the study area has equal conditions. In this paper, several optimization methods such as genetic algorithms, L-BFGS, VFCPSO and CMA-ES have been implemented to optimize the location of sensors. In this study, various sensor sensing types such as omnidirectional binary sensing model, directional sensing model and probabilistic sensing model have been used and tested for the aforementioned optimization algorithms in different Raster and Vector study areas.
This paper was focused on comparing the performance of four global optimization algorithms to optimize deployment of sensors in environment using more spatial details compared to previous approaches. The innovation of this study was to use 3D raster and vector data and to implement the global optimization methods using probabilistic sensing model to optimize sensor network placement. Finally, promising results have been presented and discussed and future methods were introduced.

Land subsidence is a vertical movement of the earth surface relative to a stable reference level. It occurs as a result of plate tectonic and human activities. The common causes of subsidence from human activities are pumping underground water, oil and gas from overlying reservoirs. Withdrawal of fluids from hydrocarbon reservoirs causes their pressure to decrease. This pressure reduction rises the stress of reservoir’s overburden sediments which was previously controlled by the pressure of inside fluids before exploitation, and consequently increases the density of their porous surroundings. If the reservoir’s density exceeds a specific threshold, overburden rocks start to subside because of their weight. Therefore pressure drawdown leads to reservoir compaction, movement of the overburden and subsidence over the reservoir. This subsidence can prove costly for production and surface facilities. So study of the subsidence caused by hydrocarbon exploitation is an important task which needs precise considerations. Several methods are available to monitor land subsidence. Classical surveying such as Leveling and global positioning system (GPS) can produce some related data whereas they are expensive and cannot also produce the needed map at a particular period of time. Recent advances in satellite and Radar technology have made it possible to measure very small movements of the earth surface. Interferometric Synthetic Aperture Radar (InSAR) is a novel technology for measuring the surface deformation. Using the InSAR technique at relatively large subsidence areas can be monitored. The pros of InSAR are that it is not necessary to physically access the deformation areas and also the high spatial and temporal resolution of its data. Sub-centimeter accuracy has been reported for InSAR derived surface deformations. Interferometric Synthetic Aperture Radar relies on repeated imaging of a given geographic location by space-borne radar platforms.
Synthetic Aperture Radar sensors measure both magnitude and phase of the transmitted electromagnetic signal that is backscattered from the earth surface. The phase measurement is used to derive information on heights and deformations of the terrain. This phase represents a combination of the distance scattering effect. If a second SAR
data set is collected then from comparing the phase of the second image with the phase of the first, an interferogram can be formed. The basic principle of interferometric SAR is that if the surface characteristics are identical for both images, the phase differences are sensitive to topography and any intrinsic change in position of a given ground reflector. The interferogram can be corrected for topographic information using an external digital elevation model (DEM). The change in distance is along the line of sight to the satellite, preventing it from directly distinguishing vertical and horizontal movement. As geometrical and temporal baseline de-correlations and atmospheric noise are limitation factors to assess slow movements in subsidence areas, recent developments in multi temporal InSAR (MTI) algorithms have enabled the detection and monitoring of the slow deformation with millimetric precision. In this paper, Marun oil field; the second-largest oil field which is located in the south west of Iran has been studied. The Small Base Line Subset (SBAS) approach that is an (InSAR) algorithm has been performed for generating mean deformation velocity map and displacement time series from a data set of subsequently acquired SAR images. SBAS technique identifies coherent pixels with phase stability over a specific observation period which has been implemented in StaMPS software. This method which is based on multiple master interferograms, works with interferograms with small spatial baselines and short temporal intervals to overcome de-correlations by increasing spatial and temporal sampling and coherent areas. For this study, we have used 10 ASAR images acquired by the ENVISAT satellite from European Space Agency (ESA) during 2003 to 2006 and have generated 22 interferograms by the SBAS method. All interferometric processing were implemented using DORIS software. A SRTM Digital Elevation Model (DEM) with 3-arcsecond geographical resolution has been used to remove the topographic phase. SBAS processing was then implemented using the Stanford Method for Persistent Scatterers (StaMPS) software. As a result, the mean velocity map obtained through InSAR time series analysis which is in the Line-Of-Sight (LOS) direction of satellite to the ground. The time series analysis results of InSAR have been then compared with field production data. This sampled data allows us to evaluate potential of non-tectonic effects such as petroleum extraction on surface displacements and the relationship between both deformation and oil production rate. The results of InSAR analysis reveal the maximum subsidence on order of 13/5 mm per year over this field due to the extraction and geological characteristics in the time period of 2003-2006.

In recent years, the growth of urbanization in Iran and the increase of migration to the major cities have led to the sudden and abnormal expansion of these cities, degradation of fertile lands and natural resources, and irreparable damages to the nature. As the population of the city of Shushtar has increased, there has been a lot of growth in the built lands in the region, causing a large change in the use of the lands around the city and the degradation of the fertile lands in the suburbs; so that, the continuation of this process could cause irreparable damages to the environmental resources of the region. Land-use prediction models are essential in planning for sustainable use of the lands (Kamusoko et al., 2009: 435, Mas et al., 2004: 94, Sohl and Claggett, 2013: 235). In addition, predicting land use changes and creating a relation between these changes and their socio-economic consequences is very important for sustainable land management (Whitford et al., 2008: 340). So far, the Markov-genetic model has been used in
several studies. Wu et al. (2006) studied the monitoring and forecasting of the Beijing region of China over a 16-year period and used the Markov chain model and regression to predict the land use. Therefore, the purpose of this study was to investigate the trend of land use changes over the past years and predicting the land use and land use changes
using the Markov chain model in the city of Shushtar in Khuzestan province. By predicting land use variations, the development and degradation of the resources can be identified and it can be led to managing the changes in the appropriate pathways (Brown et al. 2000: 247, Hathout, 2002: 229 and Jenerette et al., 2001).
The study area of this research is Shushtar city with an area of 340645.2 hectares located in the North of Khuzestan province. The software packages used in this research include ArcGIS 10.2, ENVI 4.8 and IDRISI Selva 17.0. The images used to extract ground cover classes include Landsat series satellite images; these images were used in this
research due to having a long time series, having an appropriate spatiotemporal resolution to study the land cover changes, and being free. Regarding the existing land uses in the region, the research objectives, and the capabilities of the images used to extract useful information, especially the land use mapping, four land uses including rangeland,
irrigated agricultural lands, rainfed agricultural lands and residential lands were considered. In the analysis of the Markov chain, the cover classes are used as the states of the chain. To determine the possibility of a change, the chain needs two land use maps (model inputs), which are usually obtained by processing the satellite images (Mitsova et al., 2011: 141). Markov chain analysis was performed using Markov chain order in the Idrisi Selva software. Markov chain analysis is provided for two purposes, the first matrix is used for calibration and the second one is used to simulate the possible changes occurring in the future. The output of the model also includes the possibility of transforming the state, transition area matrix for each class, and at the end of the conditional probability images for converting different uses (Gilks et al., 1996: 19 and Weng, 2002: 273).
Regarding the trend of changes during these three periods, the irrigated and residential lands classes had an increasing state, but on the contrary, rainfed lands and rangelands classes had been decreasing. The accuracy of classifications is generally more than 77%, and suitable for use in the Markov model. The results of the detection of changes in 2030 are such that if the current trend continues in the region, 20.33% will be added to the area of the irrigated agricultural land use, so that irrigated agricultural land use constitutes 60.95% of the area in 2030. This increase is due to the changes of the land uses of rangeland and rainfed to the irrigated agriculture. The decrease in the rangeland and rainfed classes will be 21.12% and 0/21% respectively which will be added to the area of the irrigated agricultural lands. These changes are more pronounced around the rural areas in the region.
During the research period, irrigated agriculture has been the most dynamic land use in the region. The area of these lands has increased from 1989 to 2015, so that, 1350131.69 ha has been added to the area of this land use during the three study periods. In the first period, the annual rate of increase was 3650 hectares and in the second period the annual rate increase was 3998 hectares. Considering the lack of change in regional governance and planning, the trend is such, that more than 60 percent of the plain area will be covered by this class in 2030 which can be led to changes in the ecosystem conditions. This result is consistent with the results of Gholamali Fard et al. (2014) in the middle coasts of Bushehr province and is not consistent with the results of Ali Mohammadi et al (2010), Dejkam et al. (2015), and
Ramezani and Jafari (2014).
In general, the results of this study indicate an increase in the area of irrigated agriculture, as well as development of the Shushtar, which has occurred through the disappearance of rangelands and rainfed lands. As it is well known, if the current strategy of land use in this area continues to reduce natural lands and increase urban lands, regardless of sustainable development considerations until 2030, significant environmental problems, including degradation of rangeland, decline in production of the major agricultural products of the region, decrease in the fertility, and increase in the deserts, will be a serious threat to the future ecosystem of the region. Also, considering the current productivity status, the region's economy which is based on the agricultural and livestock production will face a serious threat in 2030. Therefore, this research recommends the use of resulting maps to identify the sensitive areas for better planning
and management of the executive organizations.

Global warming and its consequence which occurs as climate change are of the world's major problems in the current century. Climate change and the warming of the earth have adverse effects on resources such as water, forests, pastures, agricultural land, industry and ultimately human life. The initial effect of climate change is on the atmospheric elements, particularly on the precipitation and temperature. Through evaluating long-term temperature trends we can be provided with a better insight as to how to plan for the upcoming years. Temperature is one of the elements influencing this issue. That is why monitoring and assessing its behavior is very important to humans. Therefor the simulation of these variables can be vital to gain a perception of human future.
There are various methods to simulate and predict climate variables. The most reliable one is using the data from the atmospheric general circulation models or GCM. The GCM models are only able to simulate the atmospheric general circulation data on large surfaces. The implementation of these models for long periods of time is time consuming and requires high processing speeds. To overcome this problem some simplifications should be done including a reduction in spatial resolution and removing some of the physical and thermodynamic processes at the micro scale. These simplifications increase the errors in the atmospheric circulation models and also they cause errors in the prediction and evaluation of the earth’s future climate. To solve this problem, the outputs of general circulation models are down-scaled through dynamical and statistical methods. In recent years, from the various methods of downscaling, researchers have been interested in the statistical downscaling method more than other methods. In the statistical downscaling, statistical methods such as regression and air generator models can be used. The statistical downscaling methods which also include the SDSM model, do the reducing scale based on the statistical history of large-scale predictors and the dependent variables. One of the most widely used models for downscaling GCM data, is the statistical model SDSM. In this study, the competency of this model for downscaling mean temperature was evaluated in Kermanshah station. Several data series including the mean daily temperature in Kermanshah station, data from the function of the national center for environmental prediction and the data from HadCM3 general circulation models were used under the A2 and B2 scenarios. Based on the A2 scenario a world is imagined in which the countries are operating independently, they are self-reliant, the world's population constantly increases, and economic development is region-based. And according to the B2 scenario, the population steadily increases but its growth rate is lower than the A2. The emphasis is on local solutions rather than having global solutions for economic, environmental and social stability, moderate economic development and Rapid technological changes. Kermanshah station data includes daily average from the beginning of 1961 until the end of 2010 which were used for calibration of the model. To this end, collecting the independent variables and the calibration of the model were done for the mean temperature by applying the daily temperature data of Kermanshah’s synoptic station and the data from the National Center for Environmental Prediction. In order to calibrate the observed data from Kermanshah’s station and the data from the National Center for Environmental Prediction (NCEP), it was divided into two 15-year periods (1975, 1961) and (1990 to 1976). The first 15 years was used to calibrate the model using the least square error method optimization. This work was done for the period of 40 years from 1961 to 2000. Then the mean temperature for the 10-year period 2010 -2001 data based on two basic periods of 15 years (1990-1961) and the 40 years (2000- 1961) under the two scenarios A2 and B2, were Predicted and were compared with the observed data of this period to evaluate the predicting performance of the model. The results of the evaluation period (2000-1961 and 1990-1976) using NCEP data showed that the SDSM model has an acceptable capability in simulating the variables such as the mean temperature in the evaluation period and the basic. It should be noted that with an increase in the prediction base period to 40 years, the differences according to the NCEP model and the observed data turned to zero. This can be considered as one of the model’s defects which is due to the use of linear regression because, by reducing the base period to simulate the mean temperature, the results of it, falls away from the average of the observed period, but by increasing the period duration, the outcomes will be valid. Also the amount of variance, the maximum and minimum temperature which are applied by the model to calculate the mean temperature, are not suitable and competence and it commits several errors. This can be caused by poor capability of the model to evaluate and reveal temperature fluctuations; this could be the consequence of adherence to linear regression of the model, although the station’s local conditions and the Hadcm3 model’s errors could intensify the inability.

Most of the energy consumed in the world comes from fossil fuels. Combustion of fossil fuels enters a huge amount of sulfur and nitrogen oxides, carbon monoxide and carbon dioxide in the atmosphere. Continuous increases in greenhouse gas emissions and rising fuel prices are key drivers behind more effective efforts to use renewable energy sources. Renewable energies include diverse sources of natural and accessible energy. Given that these energies are not ideal, their use reduces the consumption of oil products and creates jobs and reduces the amount of environmental pollution. The prospect of using this energy in Iran, as well as other developed countries, has become significant in the way that the government has made the necessary planning in the fifth development plan. Therefore, considering the global policies of developing these energies in our country, in order to solve problems and create employment, will be inevitable. Studies in this regard suggest that the development of the use of new energy can play a significant role in increasing the security of the country's energy system. Due to low latitudes, Iran has more capability to receive this energy. To exploit this energy, there is a need to build solar power plants. Solar panels used in solar power plants are converters of solar radiation into electrical energy. One of the most important issues in using solar energy is determining where to use it, which has a great impact on the efficiency of solar power equipment. Therefore, taking advantage of the potential of the climate can have a positive effect on the conservation of energy resources. In this
regard, it is important to identify appropriate and prone areas where solar energy is sufficient and able to replace current energies.
The required data in this study was collected from the ‘Iran’s Meteorological Organization’ for 30 years and was entered into the Excel environment and analyzed. In the Arc GIS software environment, the locations of the stations, according to their geographical coordinates, were added to the digital map of the area and the database was formed. To
prepare the map of the climatic parameters, the layer for each parameter was first prepared using the IDW interpolation method in the Geo-statistical Analyst field in the ARCGIS software environment, and then, using the AHP method, an intra-layer weight was defined. By using the ‘Reclassify’ command in the ARCGIS software, each layer was classified into several classes and each class was classified according to its importance and mapped to it. Then, to obtain a final map representing potential regions, the interlayer weight was applied according to the importance and effectiveness of each layer. Then, by overlapping the weighted layers, using the ‘Fuzzy overlay’ command in the ‘Spatial Analyst’ section, a map of all-potential regions that represents the areas with high potential for the construction of the power plant was obtained.
Discussion and In order to quantitatively evaluate the climate of solar power plants in the study area, the layers obtained from the sunshine, cloudy, dust, relative humidity, altitude and precipitation have been weighted. For this purpose, the weight of the effective indices has been obtained using the AHP model. Then, using the ‘Raster calculator’ command in the ARCGIS software, weighted difference maps were obtained, and finally, using the ‘Fuzzy overlay’ command in the same software, the final map which is a combination of overlapping of the harmonious layers, has been obtained. At last, the final map was made up of a combination of overlapping harmonious layers and the selection of the regions with the highest capacity for the construction of solar power plants.
The method used in this study is important in determining the effective indices in locating solar stations as overlapping of the harmonious layers. This method is achieved by taking into account the relative importance of all the effective indices in the final layer, which can be more credible than other methods, because this algorithm, using degree weights, gives the power to decision- makers to place more important factors which in his view affect the problem more, in the problem with the same importance and due to this superiority, the results of this method has a better resolution.. Accordingly, the results show that Fars province has a high potential in terms of solar electrical energy which in the study area, the cities of Neyriz, Estahban and Fasa are more indicative in this regard and have higher potential. It can also be concluded that the total relative weight of all indices has a greater effect on locating and cannot be determined only by one or more of the indices.

The Mean Dynamic Topography (MDT) of the seas is a quantity which comes from subtracting the Geoid Height (GH) from the Mean Sea Surface (MSS) at every point on the sea. The direction of geostrophic currents is obtained through the calculation of the MDT slope relative to the Geoid. In this research, a series of GOCE geopotential coefficients resulted from the 4 year collection of GOCE observations was used to estimate the reference geoid height in the Persian Gulf, the Oman Sea and the Indian Ocean, i.e., in the area of interest. Two MDT models data were available at the time of performing this research: Denmark Technical University’s model, named ‘Mean Dynamic Topography of Denmark Technical University 2010’ (MDT_DTU_2010) which has been available on a geographical grid of 2 arc minutes spacing (Knudsen & Andersen, 2010). This model is based on the mean sea surface topography model MSS_DTU_2010 and the 2 month of GOCE geopotential data for the Geoid as the reference surface. The second model is the Mean Dynamic Topography Centre National dEtudes Spatiales collecte localisation satellites 2009 (MDT_CNES_CLS09) with 15 minutes resolution (Rio et al, 2011). This model contains the east-west and north-south geostrophic current components with itself as well. It is based on MSS_CLS01 (Hernandez and Schaeffer, 2001) and 4.5 years of GRACE geopotential data used for the reference geoid.
In this research a new Mean Dynamic Topography (MDT) model with the name of MDT_IAU_TN_2014 is presented. Also, the surface permanent current vectors in a grid with 2 minutes resolutions is computed in the Persian Gulf, the Oman Sea and the north of Indian Ocean. This MDT is formed by a Mean Sea Surface (MSS) model computed from 6 altimetry satellites data (Topex/Poseidon, Jason 1 and 2, ERS 1 and 2 and Geosat Follow-On) and GOCE satellite data with 21 and 4 years ranges in 1992-2013 are calculated. The first step for the Mean Sea Surface (MSS) computation is to calculate the mean of Sea Surface Heights (SSH) along the repeated (in time) sub-tracks of altimetry satellites over the years available in the area of interest. The mean value of SSHs over time in a same track is then called Mean Height (MH). The Basic Radar Altimetry Toolbox (BRAT) version 3.1.0 was used for the MH computation. The correction term includes the tidal periodic variations, physical earth corrections such as troposphere, ionosphere, and sea state biases. All of these corrections are considered from the satellite handbooks T/P (AVISO/ALTIMETRY, 1996), J1 (AVISO and PODAAC USER HANDBOOK, 2012), J2 (OSTM/Jason-2 Products Handbook, 2001), ERS (RA/ATSR products - User Manual, 2001), GFO (GEOSAT Follow-On GDR User's Handbook, 2002). Among altimetry satellites, T/P (J1 and J2) has the highest orbit and longest data sets so it has been selected as a reference for corrections.
To homogenize the spectral of MSS and the Geoid, a truncated Gaussian filter with 1.386 degree radius has been used. MDT results have been compared with two global model and have 0.033 and 0.051 RMS of differences in order. Among altimetry satellites used in this research, J2 and GFO satellites have the ability to measure shallow waters. Hence, the data provided by these satellites in shallow waters, i.e. Persian Gulf are valuable. MHS differences between E1 and T/P are larger than the MHS of other satellites, because there are differences between the two missions, i.e., there are 8 km distances between E1 sub-tracks at equator but long repeatability period of 35 days of data acquisition time and T/P sub-tracks spacing are 315 km at equator and short repeatability period of 9.9 days. Also, the orbit elevations are different: T/P at altitude of 1336 km and E1 at altitude of 785 km. Inclusion of E1 data in the MSS_IAU_TN_2014 solution would globally decrease the RMS difference of the solution relative to the MSS_CNES_CLS_2011 model from 0.4 m (without E1 data) to 0.1 m. This improvement by the E1 data is probably due to the higher resolution of the data in the region of interest.
Changing the filtering radius of 1.386 degree down to lower degrees until 1 degree would increase the MDT_IAU_TN_2014 differences (relative to the MDT_DTU_2010) and MDT_CNES_CLS09 from 0.033m and 0.051m RMS up to larger values. At the 1.386 degree, the differences are minimum. For filtering radiuses of more than 1.386 degree the MDT surface would become unreasonably much smoother and the RMS difference would increase. Geostrophic and Ekman velocity currents using 22 years data of surface wind has been calculated. Total currents of the released model in this research have been compared with OSCAR in-situ data and have 0.047 and 0.031 meter RMS of differences in North-South and East-West current components. The total currents from MDT_IAU_TN-2014 model vary between 0 to 0.61 m/s in the north Indian ocean region. The comparison shows that all three models show almost the same range of variations in the region of interest. SLA an In-Situ data could be used to make the MDT_IAU_TN_2014 independent from any other models. The lack of In-Situ data in the region of interest forced MDT_IAU_TN_2014 to use MDT_DTU_2010 to cover filtered parts. Also using other gravity models with higher Spherical harmonic coefficients degree and orders such as EIGEN-6c and EGM08, would make filtering not needed in the dynamic modeling.

Land degradation process that affects the arid, semi-arid and sub-humid zones of the globe has been interpreted as desertification that great many debates have grown up around the concept. A fundamental debate has been whether desertification actually exists? If so, how it might be defined, measured and assessed (Herrmann and Hutchinson, 2005). In fact, the term "desertification" was used by Aubreville (1949) to describe the change of productive land into desert, which was the result of human activities in the tropical forest zone of Africa (Tavousi, 2010).However, the United Nations Conference on Desertification (UNCOD), held in Nairobi in 1977, launched the desertification issue into the global arena (Herrmann and Hutchinson, 2005). Desertification as defined in the United Nations Conference on Environment and Development (UNCED) and also in the United Nations Convection to Combat Desertification (UNCCD) is land degradation in arid, semi-arid and dry sub-humid areas resulting from various factors, including climatic variations and human activities (Cardy, 1993). Also, on the basis of this Convention, arid, semi-arid and sub-humid arid regions are regions in which the ratio of precipitation to potential evapotranspiration is in the range of 0.05 to 0.65 (Tavousi et al, 2010).
Determining the contribution of climatic variability to desertification is very complicated, and it is virtually impossible to separate the impacts of drought and desertification, because these processes often work together (Nicholson et al., 1998). Although now a more understanding of climatic variability has emerged, the understanding of the causes of this variability is still unfolding.
Two prevalent paradigms are expressed for climatic variability: One Internal feedback mechanisms such as Biophysical feedback mechanisms between land surface and precipitation due to modification of land cover characteristics in dry land regions and the other are External forcings, such as influence of the El-Nino Southern-Oscillation phenomenon and other major driving forces that promote changes in atmospheric circulations. Most probably, nor of these two prevalent paradigms (internal and external forcings) are mutually exclusive. Relative contributions of climate variability and human agency to desertification will likely depend on specific regional contexts (Herrmann and Hutchinson, 2005).
On the basis of UNEP index we observed that most areas of Iran have arid and semi-arid climates. With respect to the desertification intensity class, these two kinds of climates have classes of severe and very severe conditions. After those two kinds of climates, ultra arid, dry sub-humid, very humid and sub-humid climates cover most areas in Iran respectively (Alijani et al, 2015).
The purpose of this study was to investigate the trend of fluctuations in annual precipitation and the trend of UNEP aridity index of diverse climatic zones in the west and northwest of Iran.
In order to study the increase of aridity index in diverse climatic zones of the west and northwest of Iran, in the first step, the area was isolated by cutting 32 N latitude and 50 E longitude. Then, annual temperature average and total annual precipitation data was provided from 43 meteorological stations in the study area during the period of (1981-2010).
This period was divided into three decades: 1981-1990, 1991-2000 and 2001-2010. Then, for each decade, a zoning map was drawn.
In order to classify the climate, evaluate the Aridity Climatic Index and displacement of climatic zones in the northwest of Iran, the aridity index of UNEP (United Nation Environment Program) was used. Also, Kendall's nonparametric test was used to determine the significance of changes in annual precipitation.
Since the air temperature determines the potential evapotranspiration, the UNEP relationship is expressed based on the average total of annual precipitation relative to the average total of annual evapotranspiration.
Discussion and In order to analyze the change in the Aridity Coefficient for each year, the UNEP index was calculated for 43 weather stations in the west and northwest of Iran. Based on the average UNEP index in each decade, the zoning map of the Aridity Index was drawn for three consecutive decades. Then, the UNEP Aridity index was subtracted in successive decades and the change occurred in the studied area was investigated. The spatial displacement of climatic zones over these three decades, represents the increase in the aridity coefficient and expansion of the territory of arid and semiarid climate in the area.
The results clearly indicate climate change from humid climate to semi-humid arid climate and semi-humid arid climate to arid climate. Based on Aridity Index of UNEP, in most parts of the northwest of Iran investigated in this study, the coefficient of Aridity has increased from the moderate risk class to severe and very severe Aridity. Although the results of Mann-Kendall test showed that 32 stations have a negative trend, this trend is significant for the 6 stations of Urmia, Tabriz, Khoy, Miandoab, Piranshahr and Sanandaj at = 0.05 .

Energy is one of the essential components for industrial activities and the need of all people, therefore, its supply and demand is continually increasing in human societies. Population growth, its expansion and distribution, along with the ever-increasing human need for new and more efficient energy, have forced man to turn to natural renewable energies. The sun is considered to be the largest energy source in the world, which can be used in many ways. Being non-polluting, clean, free and accessible, are the important features for using renewable solar energy. Solar energy is one of the best and most economical renewable energy in Iran, which not only reduces many human concerns, such as environmental pollution, energy exhaustibility, energy conversion, etc., but also considering the climate of Iran, it can well develop in Iran. Despite the great potential of using solar energy in the country due to the intensity of radiation as well as a very good area for installation and use of solar energy, it is possible to install photovoltaic panels. Regarding the climate of Ahwaze city in terms of radiation intensity (According to the statistics of the New Energy Organization, about 4.5-5 kWh / m 2 / day) and sunny days and on the other hand, due to the establishment of important factories and large industries in the city, it faces the problem of energy and pollution caused by fossil fuels. Therefore, the study of solar energy and its potential for using solar energy to plan for the use of this energy seems necessary. Since no significant steps have been taken in this regard, this study focuses on this important issue, so that by designing a Web GIS system, one can take a step in the direction of data management and decision-making to improve the status quo.
The present research seeks to exploit renewable solar energy using solar technologies. The spatial distribution modeling of this renewable resource was performed using GIS analyses and computational intelligence. For this purpose, during the implementation of the survey, Solar Analyst Model available in ArcGIS software was used to estimate the solar radiation in the region. Also, in order to prioritize the region based on having the required potential to exploit solar photovoltaic systems, three categories of effective criteria including environmental criteria, building-density criteria and technical criteria were identified. Then, modeling was done using Fuzzy Inference System. The knowledge of available solar energy and the area of building rooftops are essential components for calculating the potential of electricity generation of photovoltaic systems, but there are technical considerations that must be taken into account in these calculations. In most cases, the calculation of photovoltaic potential requires the consideration of the output capacities of the panels. For this purpose, the technical potential of photovoltaic systems was calculated based on the formulas, the requirement of which is to estimate the geographic potential of the study area. The final stage is the design and implementation of the solar energy Web GIS system.
Discussion and Estimation of the total radiation received by the earth in the study area using Solar Analyst model, showed the total solar radiation from 0.4 to 1461 kWh per square meter per year. Also, the calculation of the geographic potential of the region and in particular the geographic potential of the rooftops, was performed using Digital Surface Model (DSM) and the results showed that major parts of the region had the potential from 1 to 49 kW per day. Technical potential of photovoltaic systems (Ei) for the roofs, was calculated using the geographical potential and its value varies from 0.1 to 138 kW per day. The results of fuzzy inference system shows that 10 square kilometers of the total area has a medium development priority and 0.7 square kilometers of the total area has a high development priority that form the highest and the lowest respectively.
Based on what has been stated so far, it can be said that the findings of the present study indicate the success of the integration of two Web GIS and solar energy knowledge in meeting predetermined objectives of the research. Utilizing this process, while providing the opportunity to assist in the decision-making process, provides web-based solar maps using spatial data. In fact, the designed system can be considered as a decision-making tool, if it allows users to view spatial information in the form of a map in addition to providing descriptive information about the region’s potential of energy generation. Users can use this system to identify appropriate locations for installing solar equipment and maximize their benefits.

The prediction of the occurrence of floods and the reduction of damages caused by it is strongly influenced by the modeling of physical phenomena and the spatial-temporal distribution of precipitation. The purpose of the research was to optimize the rainfall gauging network in Kurdistan province using Kriging estimation variance and taking into account the topography of the area. In this study, to optimize the rain gauging network in Kurdistan province, rainfall data of the rain gauging, synoptic, and climatology stations were used. In order to reduce the costs, stations close to each other that are located in the same height range and also have the same error variance, were removed from the existing network. In order to reduce the maintenance cost of the stations, after clustering of the area, 8 stations whose removal had little impact on the accuracy of the data, were identified in the province. Then. In order to strengthen the network, the optimization of new stations was put on the agenda and 28 points were set as the proposed stations.

After reviewing the existing stations’ data, 145 stations were selected for the analysis and optimization of the existing network. After selecting the normal data and spatializing them, due to the large extent of the area and the variability of the average annual precipitation, Kurdistan province is divided into smaller regions with less variations in the average rainfall. The regional division or clustering of stations is carried out using the functions available in the ArcGIS 10.2.2 software and based on the main catchment basins. In the next step, the spatial distribution of rainfall and the variance of the errors in all clusters are calculated separately. Given the importance of highlands in receiving rainfall and supplying water, the distribution of rain gauges on elevation layers has been studied. At this stage, redundant stations were eliminated, and stations which are located in close proximity of each other, and are located in the same elevation range and also have the same error variance, can be eliminated too. At the final stage, adding new stations and strengthening the network took place. At this stage, the priority is to build the station for areas where the variance of the errors is high. After adding each station, the error variance of the whole system is calculated again. Adding a new station to the network will continue as long as the network error reaches its minimum.

1-Normality test of data
After spatializing the rainfall data, their normal distribution was investigated using the Kolmogorov – Smirnov test. The results show that the distribution of data at 95% level does not have a significant difference with a normal distribution.
2- Division of the region and clustering of stations
In this study, using the region’s digital elevation map, and based on the analyses made in the software ArcGIS 10.2.2, clustering of stations and division of the region was carried out. The entire area of interest is divided into 8 clusters.
3- Calculating the Kriging error of the existing network
The amounts of the rainfall data error can be obtained by calculating the Kriging error of the existing network. As mentioned in the previous sections, the calculation of the error in the Kriging method is a function of semi-variogram (spatial structure) of the variable and this feature increases the estimation accuracy of the variable error.
4- Distribution of the stations on elevation layers and determination of the redundant stations By studying the distribution of the stations on altitudes, stations which had no impact on the accuracy of data extraction were removed. The candidate stations for removal were located in a same range of elevation, and showed similar error values. In order to be sure of the decision taken, by eliminating each station, the overall error of the network in each cluster is calculated, and an increase in the error values represents the wrong station is being removed.
5- Adding the proposed stations and calculating the variance of the new network error
Adding new stations to the network is done based on the Kriging variance. The priority of the station construction is for areas that display a high error. In the Kriging error variance method, adding a new station to the network is done based on Eó2 (error variance), in a way that points with equal error variance or greater than the value of data variance is considered as the first priority for the construction of the station. The points whose error variances are between the variance of data and ½ of the variance of data, is the second priority and finally, the third priority belongs to the points whose variances are between ½ and ¼ of the variance. In this research, based on Kriging variance, 28 stations have been proposed to strengthen the rain gauging network in Kurdistan Province.

Given that precipitation is considered as the main entrance to the planning of sustainable water resources development, in this study, the optimization of rain gauging station network in Kurdistan province was investigated using the Kriging error variance. In previous studies, generally, entropy has been considered as the main model for network modification, therefore, due to the limitations of these methods in not using the semi-variogram features, in this research, the geo-statistic method based on kriging error variance was used due to its high accuracy. The amount accuracy increase in this method depends to a large extent on the semi-variogram features (spatial structure) of the precipitation, which can be used to calculate the error variance rate for the new station before the construction and inventory of the station. In order to strengthen the network, the optimization of new stations was put on the agenda and 28 points were set as the location of the proposed stations. For practical comparison of the results, the error variance values were calculated before and after the addition of the proposed stations, the average error variance of the annual precipitation in the province decreased by 11%, with the largest decrease belonging to the central part of the province with 24.03%.

One of the practical indices in determining required energy for providing climatic comfort is the degree day index. The total mean deviation of daily temperature of human comfort temperature (threshold temperature) is called degree day temperature that provides many applications in estimating required energy in cooling and heating section. It is notable that various studies around the world have used different temperatures to calculate HDD and CDD considering their climatic and geographical location. In Iran, 18 degrees centigrade is used for HDD and 24 degrees centigrade for CDD calculation, while climatic and geographical diversity of Iran causes new base temperatures to be recommended for HDD and CDD calculations. The present study plans to present a proper base temperature for calculating HDD and CDD with regard to specific characteristics of each city's climate.
In the present study to determine the new threshold temperatures in order to provide the energy required for climatic comfort conditions, Olgyay diagram is used. Therefore, the average daily temperature and relative humidity data have been used to draw bioclimatic conditions. Since Iran has different climatic diversity, 10 stations that represent different climatic conditions of Iran were selected and analyzed (Figure 1). It should be mentioned that the duration of time series used includes the statistical period of 1950 to 2010 and these data was collected from Iran`s Meteorological Organization. Since hand drawing of each of the events on Olgyay diagram is cumbersome and time consuming considering the wide range of studied data, therefore, Olgyay diagram was digitalized to receive the output for each station quickly and easily. It is also noteworthy that in this study, Olgyay diagram is divided into 12 bioclimatic classes and the frequency of occurrence of each of the bioclimatic classes for each station in Table (1) has been reported. However, the most important section of this study is related to the determination of new base temperatures for calculating HDD and CDD indices of observational stations. Therefore, based on the days in the comfort zone, three regions in the form of percentile thresholds of 40 to 60 were selected as the representative of the central 20 percent of the data, percentile threshold of 25 to 75 percent as the representative of the dominant central 50% of the data, and finally percentile threshold of 10 to 90 as the central 80 % of the data were selected, and these domains were introduced as new thermal comfort for determining the base temperatures for HDD and CDD calculation (equation 1):
Equation 1:
In equation 1, LP is an equivalent for the threshold rank of the percentiles 10, 25, 40, 60, 75 and 90 percent, n is an equivalent for the number of samples and s is an equivalent for percentiles.
In the final step, after determining the base temperature, required cooling day-degree values (Equation 2) and heating (Equation 3) are calculated as follows:
Equation 2:
Equation 3:
In formula (2) and (3), cooling requirement is calculated by CDD and heating requirement is calculated by HDD for a given period of N days. In these formulae, T is the average daily temperature and è is the base temperature that with regard to the threshold of different percentiles, different numbers are proposed for each station.
Findings: Findings of this section showed that Shiraz and Esfahan have experienced the most ideal conditions of comfort with 35.22 and 33.22 percent of frequency of days in the comfort zone respectively and Babolsar with 83.2 percent of frequency has had the lowest percentage of days with thermal comfort. Among the observational stations, the most frequent occubasic temperature, comfort bioclimatology, Olgyay diagram, degree day index, percentiles, Iran rrence experience of frost and freezing belongs to Sanandaj, and for the stations in Makoo, Shiraz, Tehran and Tabas, the most important preventive factor for the occurrence of comfort conditions is frost and freezing. But, Jask and Bushehr have had the most experience of the days with heat stroke risks and this factor is the most important preventive factor for comfort in these two stations. Although extreme dryness is the most important preventive factor for comfort in Ahvaz, but in Rasht and Babolsar, excess moisture is the most important factor of the lack of comfort. The results indicated that Olgyay diagram has perfectly shown the climatic and bioclimatic differences of various regions. For example, for the coastal cities of the Persian Gulf and Oman Sea, the type of data distribution on the diagram showed that climatic and bioclimatic characteristics of the two cities of Bushehr and Jask differ from Ahwaz, so that the dominant climatic regime of Bushehr and Jask due to the high humidity experience, are affected by the water zone of the Persian Gulf and Oman Sea, but Ahwaz is affected both by the water body of the Persian Gulf and hot and dry systems that pass directly through the Saudi Arabia.
Based on the main objective of this research, new thermal comfort thresholds for all study stations were proposed and the results showed that according to various percentiles, minimum base temperature for calculating HDD belonged to Babolsar station and maximum base temperature for calculating CDD belonged to Shiraz station. It is also worth noting that the sensitivity of the proposed method is such, that minimum differences in the domain and base temperature of thermal comfort are visible even for the stations located in a nearly similar geographical area, and this could indicate the validity of the proposed method. Finally, monthly and annual long-term average of HDD and CDD indices were calculated for the studied cities using proposed thresholds and base temperatures. The results of this section showed that in most observational stations, the months of January, December and February have had the maximum HDD requirements and the maximum CDD requirement was calculated for the months of July and August. The research findings reveal that maximum average annual HDD and CDD requirements belong to Makoo and Jask respectively. The results of this study point to the fact that the need for heating energy has been higher than the need for cooling energy for most of the studied cities. Therefore, the findings show that, based on the proposed method, which is derived from the climatic characteristics and experimental data of each station, a more logical thermal comfort thresholds for the studied stations are presented.

Investigating the spatial andTranslation errorInvestigatingiiiii temporal variations of soil salinity plays a major role in managing the watershed and preventing the development of salinity (Mohammadi, 2007). Also, the study of groundwater salinity due to the complexity of hydrological processes, characteristics of the aquifer, and their variability is a difficult task. However, these problems exacerbate by external factors such as atmospheric conditions and human activities affecting the permeability and hydrological processes (Mirzaee and Hassan-Nia, 2013). Because of the costly nature of experiments involving salinity sampling, as well as the computational models not being calibrated and the complexity of these models in order to overcome these limitations and to determine salinity in the depths of the soil, determination of models consistent with natural behaviors and the use of existing models, Increase day by day. On the other hand, considering the fact that many lands are under cultivation in the northwest of Fars province, it is important to study the chemical properties of the soil and water in the region, including salinity.
There are various methods for studying the salinity of water and soil, for example, Syringes et al. (2006) predicted the salinity of soil profile and the drainage outlet in a research using neural networks in an experimental area in India. Arfin et al. (2003) used an artificial neural network model and linear regression model to predict the soil and water salinity. Topographic index is a measure of the extent of flow accumulation at the given point of the topographic surface. As catchment area increases and slope gradient decreases, topographic index increases. Like other combined morphometric variables, topographic index can be derived from a digital elevation model (DEM) by the sequential application of methods for local and nonlocal morphometric characteristics, followed by an arithmetic combination of the results of these calculations.
The studied watershed is located in the west of Shiraz, between the cities of Shiraz and Kazeroon. The most important urban center in this basin is the city of Bayza. The geo-location of the studied area is N 29° 12´to 29° 48´and E 52° 06´ to 52° 36´ (Figure 1). The area of the study region is 623.63 KM2. The highest and lowest altitudes in the study area are 1630 and 3083 meters respectively. The average temperature in the region is 16.8 degrees varying from 4.7 to 29.2. The study area is very rich for cultivating crops. It is also a very rich in terms of topography, geology and biodiversity. Regarding the presence of agricultural lands in this region as well as the significance of irrigation water quality and the type of soil in terms of electrical conductivity (EC), the study of the soil and water characteristics of the region is very important in terms of salinity.
The data used in this research include electrical conductivity of water and soil samples provided by Fars Agricultural Jihad Organization (2013). This region was selected considering the importance of the study region for agriculture. The zoning maps for each of them were prepared in the ArcGIS environment with the help of these sample points which were selected randomly. Then, the EC data of water and soil was homogenized and ranged from 0 to 1 with the help of membership functions. Finally, the relationship of the amount of water and soil salinity with the watershed rough terrain was investigated.
Discussion and According to the interpolation maps, it was determined that the lowest and the highest values for water salinity in the study area were 0.42 and 3.07 respectively, while for soil salinity were 0.87 and 8.75 respectively. According to the salinity zoning map prepared for soil samples in the study area, it is determined that the highest soil salinity is in the southwest of the study area, while the north and center of the study area have lower soil salinity. Also, the results of water salinity obtained by IDW method showed that the highest salinity of water is in the north of the region, while the lowest salinity of water is observed in parts of the south of the study area. The fuzzy map values of the study area are between 0.08 to 0.99, that except for a very small part of the study area located in the southeast, the rest of the area contain saline water. Also, the results of soil salinity fuzzy map of the studied area showed that the soil salinity values were between 0.61 and 0.92. In fact, the soil in the study area has a lot of salinity.
After finalizing the fuzzy map of water and soil salinity by fuzzy method, the final salinity map was classified into four classes. Values less than 0.25, between 0.25 and 0.5, 0.5 to 0.75 and more than 0.75 were classified into inappropriate, moderate, good and very good grades, respectively. (The low values: 0.75 (a-ppropriate for drinking)). Using fuzzy method for soil salinity, it was determined that 24.31% of the area was in poor class (inappropriate), 11.78 in the moderate class, 25.74 in the good class and 38.16% of the area was in the very good class, while for water salinity, it was found that 36.6% was in the moderate class, 31.69% in the good class and 31.65% was in the very good class. At the end, the relationship between the Landform map and the salinity map of the soil and water in the study area was determined. The results showed that salinity of the water in the valleys is very high, while soil salinity in the upstream drainage has shown the highest values. The results also showed that the minimum salinity of the soil and water are in the plains.

Today tourism industry comprises a large part of global economy and it is changing into the biggest and the most profitable industry through the world. Many countries have included it in their strategies and planning. Ardabil province is one of beautiful tourist destinations in northwest of Iran which attracts many tourists during all seasons. This province possesses noteworthy capabilities and potentials to create and develop tourism. Climatic condition is vital information for tourists. It is crucial to pay attention to climate features of an area and the impact that these features have on tourism formation. Climate and tourism are highly dependent on one another in such a way that having favorable climatic conditions is considered as one of the advantages and potential for tourism and many travelers take the climate conditions in selecting the time and location of their journey into consideration. Understanding the climate-threatening constraints and risks and knowledge of the attractions and latent potential of the climate features for any planning at various national, provincial and urban level including tourism are of great importance. Tourism Comfort Index (TCI) is an index that specifies climate effect on tourism systematically. The index utilizes climate elements such as temperature, precipitation, humidity, radiation and wind. In order to use the index, registered statistics in weather station is required.
The present research is descriptive-analytical and aims to determine the most appropriate time for the presence of tourists in the region and the development of tourism plans. In this research, the tourism climate index (TCI) has been used to assess the climate of tourism and climatic attractions of Ardabil province from the tourism point of view. To this end, the statistics of required climatic parameters were extracted from the climatic data of 4 synoptic stations of the province during a period of 15 years (1996-2010). After calculating the TCI index for each month of the year, The Inverse Distance Weighting (IDW) method was used to zone the comfort conditions of tourism climate of the province and to convert point data of the stations to surface data, and finally, the TCI map was obtained for the entire province.
The tourism climatic condition of Ardabil province was evaluated on a monthly basis using the tourism climate index (TCI), and the results of the surveys were presented in the form of a zoned map for each month separately. The findings of the research indicated that the months of April, May, June, July, August, September, October and November with good, very good, excellent and ideal ratings have the best conditions for the presence of tourists in the region and the of December, January, February and March as the cold months of the year, have unfavorable climatic comfort conditions in the region and the province, does not have an appropriate condition for the presence of tourists.
Ardabil province is distinguished from other regions due to the diversity of its climate and its unique natural, historical and cultural attractions, and may be considered as the country’s tourism hub. Due to the numerous capabilities of the province in attracting tourists, it is necessary to recognize and evaluate the climate of comfort through acceptable scientific methods in order to systematically determine the impact of climatic factors on tourist's activities. The results indicate that the comfort climate of the province is very diverse in different seasons of the year, so that in the warm seasons, the southern regions of the province have a favorable situation and in the cold seasons of the year this trend changes and the northern areas of the province have favorable situation.

The worn out textures are one of a variety of urban textures that are spatially unstable due to physical deterioration and inappropriate enjoyment and the existence of vulnerable infrastructures and are one of the main challenges facing most cities and especially metropolises. Currently, 53 spots of worn-out texture spots have been identified in 15 regions of Esfahan which now have 350,000 inhabitants living in these areas. Meanwhile, the extent of the worn out texture in the Isfahan’s region 5 is 69.75 hectares. This article seeks the extent to which the identification and prioritization of worn out textures within the scope of the study area can affect the revival and reduction of their vulnerability.
Regarding the purpose of the research, this research is an applied type and the method of studying and analyzing information and data is a descriptive-survey method. According to the country's standards, the criteria of the gravels fineness, impermeability, and instability have been taken into considerations, and analyses were carried out using the AHP model and the GIS software.
The main objective of this research is to prioritize worn out textures for restoration and renovation, the first step of which is to identify such textures. The criteria and indices which have been considered in this research include: material genus, the degree of deterioration, the age and the extent of the real estate, and to analyze the desirability and prioritization of worn out texture in order to modify the texture erosion, the indexes should be scored after defining the criteria and indexes and performing the initial classification. In the next step, scoring of the internal values of each of the indices was done and in the final stage, the coefficient of importance of each criterion was determined using the AHP model and the Thomas hourly table and the criteria with the same weight were combined and the final output was obtained.
The results indicate that the lack of facilities, utilities and infrastructure has led to the migration of local residents to other areas and negatively impacted the population growth rate in recent years. On the other hand, due to the socioeconomic condition of the inhabitants of the texture, the process of reconstruction and renovation inside the texture has slowed down and this factor has exacerbated the deterioration of the texture.

Urban heating is one of the most well-known forms of local manipulation of the climate by mankind, so that changes in the use of land cover in urban areas can lead to an increase in urban temperatures relative to the air temperature in rural areas. This phenomenon has been quantified in the form of the Urban Heat Islands (UHI) and has been studied and recorded for over 150 years in various cities of the world. The effect of the Urban Heat Island refers to an increase in the temperature of each man-made area, with respect to the surrounding surfaces. This phenomenon in urban areas refers to an increase in the temperature of cities with respect to the rural and suburban areas. On the other hand, the heat island directly affects the health of urban wildlife. Each year, in the United States, about 1,000 animals die due to the temperature rise, and more than that are destroyed because of the urban air harmful compounds. These changes in the pattern of winds have very important and dangerous consequences, such as the transmission of air pollution and dispersed toxic particles from cities to the suburbs, to disruption the people’s comfort within the city, which is why the heat islands are now considered as the causes of worrying about people’s health. Moreover, the heat islands change the wind patterns in the cities and surrounding areas. The suburban breeze is a dominant phenomenon in cities that are located on a flat land. The presence of heat islands, in addition to temperature changes, causes changes in land processes such as early flourishing of urban plants and longer growing season.
The present research has been an applied research in terms of targeting and a field-analytical one in terms of data collection. In order to reach the final goal of the research, the meteorological statistics of the synoptic meteorological station of Urmia city was studied first. Then, the study of different regions of the city was done in terms of temperature given the 9 stations set up inside the city and the suburbs. The data of 9 stations set up in the city was adjusted by installing a dry temperature sensor at an altitude of 180 cm, in cooperation with the municipality of Urmia, at a minimum and maximum daily rate of two hours (7:30 am and 5:30 pm) in hourly, daily and monthly forms. It should be noted that, the desired statistical period is from April 21, 2015 to July 22, 2015, and the readout pattern is on a daily basis, and its output is in the form of 1st to 4th of each month (days 7, 15, 22 and 29 of each month).
Result and The rapid growth of urbanization and the increase in the population of Urmia city has caused significant changes in the physical and natural conditions of the city. This increase and expansion of the urbanization trend has affected some of the meteorological quantities in a way that, the performed studies indicate that the minimum temperature of Urmia city during the twenty year period is increasing in all months of the year compared with the neighboring stations. Nevertheless, specifying the limits of the Urmia heat island requires more precise studies. The study of the isothermal map of the average maximum temperature in the months of May, June and July, 2015 indicates that the Velayat-e-Faqih square station with a temperature of 29.41 degrees Celsius accounts for the highest temperature compared with eight other stations and in fact, has formed the center of the heat island. At the same time, the station for the license plate exchange center in the city of Urmia with a maximum temperature of 22.27 Celsius, is the coolest station compared to other stations, indicating a heat difference of 6.64 Celsius in the city. According to the above map, the intensity of the heat island decreases by distancing from center of the city. But the most important result that can be obtained from the above maps is the extension of maximum temperature curve toward parts of the East and South-east. The reasons for the high average temperature at the station of the municipality town and the station of Golman Khane can be summarized as follows: The existence of 90% of industrial uses, workshops and factories at the edge of these stations Wind flow Given that wind is the most effective barrier against the formation of heat islands, the combination of the wind field with the pattern of heat island’s spatial variations shows significant results, which is a sign of the great impact of wind on the quality of formation of the heat island. The wind contributes to the extension of the heat island’s curve through the transfer of suspended particles and gases existing in the urban atmosphere.

Global warming and climate change are terms for the observed century-scale rise in the average temperature of the Earth's climate system and its related effects. Multiple lines of scientific evidence show that the climate system is warming. Many of the observed changes since the 1950s are unprecedented over tens to thousands of years. In 2014, the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report concluded that "It is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century. The largest human influence has been emission of greenhouse gases such as carbon dioxide, methane and nitrous oxide. Human activities have led to carbon dioxide concentrations above levels not seen in hundreds of thousands of years. Climate model projections summarized in the report indicated that during the 21st century, the global surface temperature is likely to rise a further 0.3 to 1.7 °C (0.5 to 3.1 °F) for the lowest emissions scenario and 2.6 to 4.8 °C (4.7 to 8.6 °F) in the highest emissions scenario. These findings have been recognized by the national science academies of the major industrialized nations and are not disputed by any scientific body of national or international standing.
Climate change is one of the main challenges that human being has faced since the 19th century. Anthropogenic changes in climate which leads to global warming and various side effects occurred and affected human life. The global warming leads to some significant changes in environmental, ecological and economic conditions. The spatiotemporal dynamics of vegetation colony and various biodiversity dynamics are also related to global warming. One of the main signal of global warming is the significant trends and changes in some climatic factors such as monthly, daily and annual temperature and rainfall. The spatial dynamics of climatic factors such as temperature and rainfall could also be related to global warming. In this study, we aimed to investigate the rainfall variations in different altitude ranges in Iran.
Precipitation varies from year to year and over decades, and changes in amount, intensity, frequency, and type (e.g. snow vs. rain) affect the environment and society. Steady moderate rains soak into the soil and benefit plants, while the same amounts of rainfall in a short period of time may cause local flooding and runoff, leaving soils much drier at the end of the day. Snow may remain on the ground for some months before it melts and runs off. Even with identical amounts, the climate can be very different if the frequency and intensity of precipitation differ, as illustrated, and in general the climate is changing from being more like that at Station (Stn) to that at Stn A. These examples highlight the fact that the characteristics of precipitation are just as vital as the amount, in terms of the effects on the soil moisture and stream flow. Hydrological extreme events are typically defined as floods and droughts. Floods are associated with extremes in rainfall (from tropical storms, thunderstorms, orographic rainfall, widespread extra-tropical cyclones, etc.), while droughts are associated with a lack of precipitation and often extremely high temperatures that contribute to drying. Floods are often fairly local and develop on short time scales, while droughts are extensive and develop over months or years. Both can be mitigated; floods by good drainage systems and drought by irrigation, for instance. Nonetheless, daily newspaper headlines of floods and droughts reflect the critical importance of the water cycle, in particular precipitation, in human affairs. World flood damage estimates are in the billions of U.S. dollars annually, with 1000s of lives lost; while drought costs are of similar magnitude and often lead to devastating wildfires and heat waves. The loss of life and property from extreme hydrological events has therefore caused society to focus on the causes and predictability of these events. Tropical cyclones typically have the highest property damage loss of any extreme event, and are therefore of great interest to state and local disaster preparedness organizations, as well as to the insurance industry.
The data of annual rainfall of 22 synoptic stations has been investigated during 1992 to 2012. First, we sorted these stations based on the altitude ranges into 4 classes, namely: Less than 500 meter, 500 to 1000 meters, 1000 to 1500 and more than 1500 meter above sea level. We used Man-Kendal’s nonparametric trend analysis test to detect any significant trend at 95 and 99 confidence levels (P value= 0.05 and 0.01, respectively).
Discussion and The results indicated that the highest rainfall decrease was observed at the elevations below 500 meters, especially in March and in the annual scale. The highest precipitation at the elevations of 500 to 1000 meters was observed in the months of March, May and October, with the highest drop in rainfall at 1000 to 1500 meters in February and June. On the annual scale, all stations showed a negative trend in rainfall. Many stations, including Maragheh, Maku, Mahabad, Urmia and Birjand, showed a significant decrease in annual scale. The results of this study showed that elevations above 1000 meters have a higher relative stability in rainfall, while rainfall at stations below 500 meter elevations have a more time variability.
Based on the findings of this research, it can be concluded that the monthly and annual rainfall of stations located at elevations below 1000 meters have had greater and more significant changes than the rest of the stations. Thus, it can be said that the climate change has been more noticeable in the stations of this class.

Paying attention to sustainable urban physical development in urban development programs indicates the importance of this issue in strengthening the cultural, social and physical aspects of the city. Developers in developing countries have deeply realized that infrastructure services and facilities have also played a major role in improving the development of urban and rural areas in these countries, and emphasizes this. Finding out that improving the access of urban and rural communities to basic services is an important tool in accelerating regional development, and accepts that location-based services, in addition to impacting costs in Efficiency and utilization and their quality are also effective.
A lot of research has been done in the field of location, including the study of Sin et al. (2002) aimed at evaluating urban land use structures with an eye to sustainable development. Simpleiara et al. (2004) examined the dynamics and modeling of urban expansion with the help of GIS in the city of Manglor, India, and predicted the type of future expansion of the city. Vanakata Subways (2007) completed the article entitled "Analysis of Places for Urban Development using GIS" (Chang, 2008) using GIS and Land Multi-Fuzzy Decision-Making Model Has identified susceptible people for the establishment of an urban forest in Harlingen.
The importance and necessity of this research in the lack of methods are suitable models for locating human settlements. In decision-making for the development of human settlements, all the criteria and parameters required and involved in structured and structured models should be considered in the form of up-to-date models. The purpose of the research is to develop a suitable model for determining the appropriate sites for the development of human settlements. In this research, we have been asked to answer the following question. Is the city of Kashan capable of urban development and, if so, what is its potential and in what districts?
The city of Kashan with an area of 20,000 square kilometers (2100 hectares) and a population of 500,000, facing the mountains on one side with its back to the desert on the other side, is located in the central region of Iran. The geographical coordinates of Kashan with an altitude of 945 meters above the sea level are 51 degrees and 27 minutes East longitude and 33 degrees and 59 minutes North latitude.
In this research, at first, data were collected and the criteria were defined and weighted by FANP. Then, using the Arc Gis software, the criterion map was created and standardized. To create the final map, the layers were combined and overlaid by the weighted linear combination method and Gamma function in fuzzy logic. Finally, the attraction map of Kashan City for urban development was created and analyzed.
The GIS-based linear weighting method (WLC) includes the following steps: 1. Defining a set of evaluation criteria and options
2. Standardize the mapping layer of each level
3. Define the weight for each criterion: meaning that a relative weight is assigned directly to each criterion map.
4. Generating the layers of standardized layer with weight: This means that we multiply the standardized layers of the weight in the respective weights.
5. Add the final score to each option using the "Gamma" for the layout of the standard weighted map.
6. Sorting options based on ratings (the best option is the option with the highest score).
Result and To determine the weight and prioritization of the FUZZY ANP software criteria, the purpose of the research which is suitable for urban development, is at the highest level of decision-making, and at the next level, the criteria Includes (environmental, socioeconomic and physical), and at the last level are the following criteria which are mentioned in the article 13 at the beginning of the article, and according to experts, regarding the recognition of the region the weight loss study is carried out for each of the following criteria. After weighting and performing calculations in the software, the final weight is obtained. In urban development, the highest weights are taken to the slope index and the lowest weight is considered as the index of slope (Table 2). After fuzzying and multiplying the weights by the fuzzy layers, the GAMA operator with three suffixes (0.9, 0.5, 0.1), is applied to the fuzzy layers which is shown in Fig. 7. The 0.9 gamma fuzzy operator shows the most compatible among the urban areas of Kashan with appropriate lands for urban development. Therefore, a 0.9 gamma is referred to as the final layer of appropriate land for urban use. The second coexistence method is, using the WLC linear gravity combination. In this section, all cabinet layers were classified instead of fuzzy layers, and their class values were determined. Then, in the RASTER CALCULATOR, the classified layers were multiplied by the weight of the FANP, and finally, the total layers were plotted, as shown in Fig. 8.
Based on the results of this research and the previous studies, the optimal result is the time taken by the 0.5 gamma operator, in which case its function is a combination of two operators Sam and Product. According to the final map obtained from the WLC method, urban development is more possible in the southwestern part of the city of Kashan. In the fuzzy method, the results indicate that the current location of Kashan city and its southern regions have good potential. The results of the linear weight combination method are similar to the fuzzy combination method of the current location of Kashan and its southern and southwestern regions. About 15% of the total area of the city of Kashan is suitable for urban development. Therefore, according to the obtained results, the aforementioned model including two methods and the use of the decision-making techniques, can be used as an appropriate model for studying the power of other similar regions (central regions of Iran). The development of the cities of Kashan and Qasr is more oriented towards the south and southwest.