نشریه تحلیل فضایی مخاطرات محیطی
سال هشتم شماره 3 (پاییز 1400)

The relation between society and environment has gone through different phases. During the years before the World War II, the environmental determinism controlled this relation. However, after the 1950’s the anthropocentrism replaced the environmental determinism and humans began to overuse the nature in such a way that nature lost its sustainability and many hazards and crises occurred. These destructions were so intense and widespread that some researchers compared with the episodes of geologic time and named the era beginning from 1970’s the Anthropocene epoch. During this period, the planetary boundaries were crossed in some areas such climate change, nitrogen cycle and biodiversity. Climate change has created most of other hazards. To overcome these problems in 1978 the Brandtland report   announced the sustainable development as not to spend resources more than the nature’s production capacity and not to pollute the nature more than it can assimilate. In other words, the nature should remain in its sustainable state so that the future generations can live with no limitations. The principles of the sustainable development were defined in the earth summit of Rio in 1992 such as social equity, economic viability, and environmental sustainability. These principles were broken down in 17 goals. The Rio conference asked all countries to achieve the sustainable development goals by 2030.  Methodologically the sustainable development requires integrated multidisciplinary approach to investigate the complex system of human- environment in different temporal and spatial scales to achieve the social equity, economic viability, and environmental sustainability. For this reason, many disciplines such as natural resources, environmental sciences, ecology and geography have contributed to the field. Different data from natural resources, human needs and drivers and environmental changes are required to process in very complicated models. In addition to different variables, the hazards are very important component of the sustainable development research, which also requires multi-aspect complicated approach and models. Spatial dependency is another aspect of sustainable development as it differs from place to place in many characteristics. In brief, from the spatial perspective the sustainable development is an integrated multi-approach research about the human-environment system to establish the sustainability on the earth. All of the related fields should study the sustainable development in collaboration with each other. However, the geography due to its long history of studying the relation between human and environment and its spatial dependency is the best single scientific field which can afford studying the sustainable development. Since the earliest times geography has developed quantitative methods, spatial techniques such as geostatistics, and environmental ethics to conserve the nature and human prosperity. The multi approach and systematic works are the main characteristics of Geography. On the other hand, Geography’s vision of defining the location for human’s activities while saving the nature’s sustainability covers the sustainable development completely. Therefore, geography is the overarching field for the sustainable development and it is the main intention of geographers to research and implement the sustainable development to reduce the environmental hazards and develop the sustainable environment for all the human beings at present and in the future. Geography studies the sustainable development through three steps including spatial analysis, spatial planning, and spatial management. In addition, geographers should learn different skills such remote sensing, multivariate statistics and above all develop a common language between different branches of geography.

Panel reports on climate change suggest that climate change around the world is most likely due to human factors. Temperature and precipitation are two important parameters in the climate of a region whose variations and fluctuations affect different areas such as agriculture, energy, tourism and so on. Seymareh basin is one of the most significant sub-basins of Karkheh. The purpose of this study is to predict the impact of climate change on precipitation and temperature of the Seymareh Basin in 2021-2040 period. These effects were analyzed at selected stations with uncertainties related to atmospheric general circulation models (GCMs) of CMIP5 models under two scenarios of RCP45 and RCP85 through LARS-WG statistical model. Then the uncertainties of the models and scenarios were investigated by comparing the monthly outputs of the models by the coefficients of determination coefficient (R2) in the forthcoming period (2021-2040) with the base period (1980–2010). The root mean square error (RMSE) calculations presented the best model and scenarios for generating future temperature and precipitation data.The Seymareh catchment is the largest and the main Karkheh sub-basin that covers parts of Kermanshah, Lorestan and Ilam provinces. The length of the largest river at the basin level to the site of the Seymareh Reservoir Dam is approximately 475 km, and the area of the basin is 26,700 km2. Geographic coordinates of the basin are from 33° 16 ́ 03 ̋to 34°59 ́ 29 ̋north latitudes and 46°6 ́9 ̋to ̋ 5 ́ 0 ° 49 Eastern longitudes, minimum basin height 698 m at the dam outlet and its maximum height 3,638 m. It is on the western highlands of Borujerd. The information used in this study was obtained from the Meteorological Organization of the country. For this study, three synoptic stations of Kermanshah, Hamadan and Khorramabad, which had the highest statistical records and had appropriate distribution at basin level, were used. These data included daily and monthly temperature and precipitation information, and sunshine hours. The LARS-WG fine-scale exponential model was proposed by Rasko et al., Semnoff and Barrow (1981). We used daily data at stations under current and future weather conditions. In order to select the best GCM model from the models mentioned above, minimum temperature, maximum temperature, precipitation and sunshine data were entered daily in the base period (1980–2010) and data were generated for five models under two scenarios of RCP45 and RCP85 for the period 2040–2021. The data were generated in 100 random series and the mean of required variables (minimum temperature, maximum temperature and rainfall) were extracted monthly in the period 2021-2040. Then, root mean square error (RMSE) and determination coefficient (R2) were used to evaluate the performance of the models and compare the results. To ensure the models' ability to generate data in the coming period, computational data from the model and observational data at the stations under study should have been compared. The capability of the LARS-WG model in modeling the minimum temperature, maximum temperature, and radiation at the stations under study was completely consistent with the observed data. The model's ability to exemplify rainfall was also acceptable, however the highest modeling error was related to March rainfall. By comparing the observed and produced data including monthly average precipitation, minimum and maximum temperatures through five mentioned models with their indices, the best model and scenario for future fabrication were determined. The results of this comparison showed that among the available models, HADGEM2-ES model under RCP 4.5 scenario had the best result for precipitation and HADGEM2-ES under RCP 8.5 scenario predicted the best result for maximum temperature. Determining the best model, precipitation data, minimum temperature and maximum temperature produced in the selected models and scenarios were analyzed to investigate the climate change temperature and precipitation for the future period. The results of this study indicated that due to the wide range of output variations of different models and scenarios, by not taking into account the uncertainties of the models and scenarios can have a great impact on the results of the studies. It was also found in this study that the LARS-WG exponential model was capable of modeling precipitation data and baseline temperature in the study area, so that the radiation data, minimum and maximum temperatures were completely consistent with the data. The observations are consistent and the models' ability to predict rainfall is very good and acceptable manner. In investigating the uncertainties caused by atmospheric general circulation models and existing scenarios, the best model to predict precipitation in the study area is HADGEM2-ES model under RCP 8.5 scenario, the best model for temperature estimation model HADGEM2-ES under RCP scenario No. 4.5. The overall results of this study revealed that the average precipitation in the basin will decrease by 4.5% on average, while the minimum temperature will be 1.5° C and the maximum temperature will be 2.17° C. The highest increase will be due to the warmer months of the year. Notable are the disruptions of rainfall distribution and the high temperatures will have significantly negative consequences than rainfall reduction.

Panel reports on climate change suggest that climate change around the world is most likely due to human factors. Temperature and precipitation are two important parameters in the climate of a region whose variations and fluctuations affect different areas such as agriculture, energy, tourism and so on. Seymareh basin is one of the most significant sub-basins of Karkheh. The purpose of this study is to predict the impact of climate change on precipitation and temperature of the Seymareh Basin in 2021-2040 period. These effects were analyzed at selected stations with uncertainties related to atmospheric general circulation models (GCMs) of CMIP5 models under two scenarios of RCP45 and RCP85 through LARS-WG statistical model. Then the uncertainties of the models and scenarios were investigated by comparing the monthly outputs of the models by the coefficients of determination coefficient (R2) in the forthcoming period (2021-2040) with the base period (1980–2010). The root mean square error (RMSE) calculations presented the best model and scenarios for generating future temperature and precipitation data.             The Seymareh catchment is the largest and the main Karkheh sub-basin that covers parts of Kermanshah, Lorestan and Ilam provinces. The length of the largest river at the basin level to the site of the Seymareh Reservoir Dam is approximately 475 km, and the area of the basin is 26,700 km2. Geographic coordinates of the basin are from 33° 16 ́ 03 ̋to 34°59 ́ 29 ̋north latitudes and 46°6 ́9 ̋to ̋ 5 ́ 0 ° 49 Eastern longitudes, minimum basin height 698 m at the dam outlet and its maximum height 3,638 m. It is on the western highlands of Borujerd. The information used in this study was obtained from the Meteorological Organization of the country. For this study, three synoptic stations of Kermanshah, Hamadan and Khorramabad, which had the highest statistical records and had appropriate distribution at basin level, were used. These data included daily and monthly temperature and precipitation information, and sunshine hours. The LARS-WG fine-scale exponential model was proposed by Rasko et al., Semnoff and Barrow (1981). We used daily data at stations under current and future weather conditions. In order to select the best GCM model from the models mentioned above, minimum temperature, maximum temperature, precipitation and sunshine data were entered daily in the base period (1980–2010) and data were generated for five models under two scenarios of RCP45 and RCP85 for the period 2040–2021. The data were generated in 100 random series and the mean of required variables (minimum temperature, maximum temperature and rainfall) were extracted monthly in the period 2021-2040. Then, root mean square error (RMSE) and determination coefficient (R2) were used to evaluate the performance of the models and compare the results. To ensure the models' ability to generate data in the coming period, computational data from the model and observational data at the stations under study should have been compared. The capability of the LARS-WG model in modeling the minimum temperature, maximum temperature, and radiation at the stations under study was completely consistent with the observed data. The model's ability to exemplify rainfall was also acceptable, however the highest modeling error was related to March rainfall. By comparing the observed and produced data including monthly average precipitation, minimum and maximum temperatures through five mentioned models with their indices, the best model and scenario for future fabrication were determined. The results of this comparison showed that among the available models, HADGEM2-ES model under RCP 4.5 scenario had the best result for precipitation and HADGEM2-ES under RCP 8.5 scenario predicted the best result for maximum temperature. Determining the best model, precipitation data, minimum temperature and maximum temperature produced in the selected models and scenarios were analyzed to investigate the climate change temperature and precipitation for the future period. The results of this study indicated that due to the wide range of output variations of different models and scenarios, by not taking into account the uncertainties of the models and scenarios can have a great impact on the results of the studies. It was also found in this study that the LARS-WG exponential model was capable of modeling precipitation data and baseline temperature in the study area, so that the radiation data, minimum and maximum temperatures were completely consistent with the data. The observations are consistent and the models' ability to predict rainfall is very good and acceptable manner. In investigating the uncertainties caused by atmospheric general circulation models and existing scenarios, the best model to predict precipitation in the study area is HADGEM2-ES model under RCP 8.5 scenario, the best model for temperature estimation model HADGEM2-ES under RCP scenario No. 4.5. The overall results of this study revealed that the average precipitation in the basin will decrease by 4.5% on average, while the minimum temperature will be 1.5° C and the maximum temperature will be 2.17° C. The highest increase will be due to the warmer months of the year. Notable are the disruptions of rainfall distribution and the high temperatures will have significantly negative consequences than rainfall reduction.

Light pollution generally refers to an unplanned increase in artificial lighting and the consequent change in light levels is not guided (Lu, 2002). Light pollution is called standard pollution at an inappropriate time or place and is said to be annoying and polluting the environment and the night sky.Studies show that excessive exposure to artificial light, especially in the dark hours of the night, can be considered as light pollution and adversely affect the environment and humans. Studies show that excessive exposure to artificial light, especially in the dark hours of the night, can be considered as light pollution and adversely affect the environment and humans. The exponential growth of population and the rapid rate of urbanization and industrialization in Iran has significantly increased the amount of artificial light at night and increased the amount of light pollution. There are various tools for assessing night light variations, including operational linear satellite scanning data for the Meteorological Defense Satellite Program (DMSP / OLS). This data not only helps in assessing the severity of light pollution but can also be used as a tool for risk management and high-risk zoning and susceptibility of this pollution. This study attempts to analyze the spatio-temporal pattern of light pollution in Iran.

This study was conducted at national and provincial level. DMSP / OLS night light images were used as data for this study. The data were downloaded from the National Geophysical Data Center (NGDC) Office of the National Oceanic and Atmospheric Administration (NOAA). The brightness in these images reflects the night light in residential areas of DMSP / OLS night optical illumination from six satellites (F10, F12, F13, F14, F15 and F16) and the spatial resolution of these images is 850 meters. The calibrated digital data of the DMSP / OLS satellite are digital numbers (DN) of each pixel between zero and 63 and were therefore classified into 6 classes in order to better analyze the images was used. Classes with digital numbers (DN) less than 1 are as areas without luminosity, 1/12/4 with very low luminance, 12/24/4/8/8 with low luminosity, 24/37/2/2 with Moderate luminosity, 37 / 49-2 / 37 high brightness and 49-6 / 63 high brightness areas. The rate of change of digital number (DN) at the national and provincial levels, as well as the percentage and area of ​​each class in each time period, and the rate of change in each class over the period 1991 to 2001, 2001 to 2004, 2004 to 2006, 2006 to 2011, 2011 to 2013. In order to investigate the effect of human factors on night light changes, the relationship between night light and relative population density at country and provincial level and its variation over time periods were studied and statistical relationship between them was calculated.
Discussion and

The three provinces that occupy most of the area with the most glare in the provinces are: Tehran with 2621 square kilometers, Khuzestan with 2214 square kilometers (Figure E2), 3- Isfahan with 1891 sq. Km. In addition, the lowest luminosity area belongs to the three North Khorasan provinces (95 km2), South Khorasan (118 km2) and Ardabil province (127 km2). Have earned their own. mong the provinces of the country, DMSP / OLS Satellite and Satellite Provinces in 2013 are the most glare-free region of the country, covering an area of ​​about 168002 km, followed by Kerman provinces with 161800 km and Yazd with 121491 sq km is next in rank. The highest relative density of the country was observed in Tehran provinces (654 people / km2), Alborz (270 people / km2), respectively. This high relative density of population in these two provinces has increased the amount of artificial light produced so that Tehran province accounts for the highest percentage of night light area with very high brightness (8.8%) in 1996 and a total of 0.5%. 46% of the province is in the range of light with very low, low, medium, high and very high brightness, and the rest of the province lacks brightness at night, which accounts for the least percentage of night light in the country. Is. Alborz province has the second highest relative density of population in the year 1996 and at the same time after Tehran province has the highest brightness of light with 5/16.

The results of this study show that the amount of night light in the country has been steadily increasing from 1996 to 2013, and the percentage of the area with very low brightness has increased by 25.8%, for the low brightness area (111.8%). , Increased in the region with moderate luminosity 142.5%, in high luminosity region (140.2%), and in high luminosity region 156.8%, which could be a warning for the spread of light pollution in the country.. In 2013, the two provinces of Tehran, Alborz and Tehran provinces had the highest amount of artificial light in terms of area and percentage of the area with high brightness at night, and Khuzestan, Bushehr, Fars and Isfahan provinces. There are other provinces that rank next.
 

Anticlines are the most prominent surface landforms whose geometry and morphology reflect mechanism of their formation and are keys to assessing the existence of deep faults that are effective in their formation and are among the most important seismic sources.Detachment folds are formed by buckling of the rock units in response to shortening and are typically symmetric folds. Alternatively, asymmetric folds at the surface may be forced by the propagation of thrust faults at depth (fault propagation folds) or result from thrust movements along footwall ramps in the sedimentary pile (fault-ramp folds).The Zagros folds have often been interpreted as completely detached along the Hormuz salt.Structurally, the study area is a part of the folded and coastal Zagros whose geological structure is simple and gentle and comprises a series of near-compact anticlines with a near-vertical axial surface and a northwest-southeast trend.Outcrops of lithological formations in the study area include Surmeh, Fahliyan, Gadvan, Dariyan, Kazhdumi, Sarvak, Ilam, Gurpi, Pabdeh, Gachsaran, Mishan, Aghajari and Bakhtiari. In the northwestern part of the Kangan anticline, uplift of salt diapir along the Darang Fault has led to the exposure of limestone, shale, dolomite and anhydrite units of the Khami Group.Assaluyeh is one of the most important economic bases in Iran and also one of the largest energy production areas in the world. With the rapid development of Assaluyeh region and increase of residential, urban and industrial constructions and refinery facilities, without attention to environmental hazards and especially earthquakes, it seems necessary to conduct this research.The aim of this study was to investigate the morphometric characteristics of the Assaluyeh, Veravi and Kangan anticlines and its relationship with active tectonics in the region.

At first, topographic, drainage network, slope, slope direction and tectonic maps of the anticlines were prepared using digital elevation model data, Landsat imagery and field surveys. Then, the geomorphic quantitative indices of the fold front sinuosity, aspect ratio, fold symmetry index, fold surface symmetry index, anticline crestline index, fold elevation index and spacing ratio were calculated. Qualitative studies were carried out on drainage pattern indices, triangular facets, wineglass valleys, linear valleys, fault scarps, springs, alluvial fans, etc. Finally, the relationship between all geomorphic and tectonic parameters was analyzed.

Fold symmetry index is one of the most important parameters that show the degree of inequality of the two limbs of the anticline and thus the intensity of tectonic activity. In a completely symmetric anticline, the value of this index is 1, while in an asymmetric anticline the value of this index is less than 1. The index values for all three anticlines are less than one, but the Asalouyeh anticline shows more asymmetry, indicating a high tectonic activity on the anticline.The fold front sinuosity index indicates the degree of tectonic activity or age of the folding system. The values obtained for this index in the three anticlines indicate that the anticlines are young and the tectonic forces are dominating the erosion.
The high value of the aspect ratios indicates the elliptical shape of the anticline, which is caused by the high stress perpendicular to the axis of the anticline. The index for Varai, Kangan, and Asalouyeh Anticlines are 0.7, 0.5 and 0.5, respectively, which again indicates nearly high tectonic activity in all three anticlines.The spacing ratio index at the northern flank of Varavi and Assalouyeh anticlines and the southern flank of  Kagan anticline indicate a high value. Quantitative index of surface symmetry of folds also shows that all three anticlines are asymmetric and the asymmetry of Asalouyeh anticline is greater than Kangan and Varavi anticlines.The drainage pattern is another indicator that, in the absence of tectonic evidence, can be a key to identifying tectonic activity.The existence of asymmetric fork drainage networks is evidence of active tectonic evidence indicating lateral growth of anticlines. According to this criterion, Varavi anticline has grown to the northwest.Comparison of the valleys shows that most of the valleys in Kagan anticline are of wineglass type whereas in Asalouyeh and Kangan anticlines linear valleys are more abundant. Some of these valleys are formed along transverse faults. The presence of numerous alluvial fans in the slopes of the Varavi anticline, indicates rapid erosion of the valley bed due to the rapid uplift and increasing valley slope. The presence of elongated and narrow V-shaped valleys is another evidence of the high tectonic activity of this anticline.

In seismicity studies and identification of hidden or blind fault studies, geophysical and geotechnical methods are expensive, time-consuming and require special equipment and are performed on a small scale. With the availability of landforms and features, risk assessment will be done at a lower cost, faster, and on a larger scale, if a relationship between landscapes and earthquakes can be established.The geometry of the folds reflects the mechanism of their formation. Asymmetrical folds are associated with deep faulting and a detachment horizon, where the movement of sedimentary layers on the detachment horizon or at the tip of the hidden faults can cause an earthquake. The three anticlines of Assaluyeh, Varavi and Kangan are also part of the folded Zagros and have the characteristics of the folded Zagros.In this study we defined a new index related to fold morphology, called fold surface symmetry index. Also we used fold morphology to detect the presence of detachment horizons and faults in the core of anticlines and their relationship to seismic hazard risk.The results of this study show the transverse profile asymmetry of all three anticlines due to the association of these anticlines with the longitudinal faults in the anticline core and along their axes. The results of measurements of aspect ratios, fold front sinusitis, anticline ridge, and study of drainage patterns and tectonic landforms such as fault scarps, triangular facets, linear valleys also confirm the high tectonic activity of all three anticlines and the potential for earthquake hazard due to the movement of deep faults or any segments of them.

Participation in social affairs is a commitment and acceptance of individual and social responsibility that all human beings will have to accept. This commitment and responsibilities may take the form of definite and unlimited activities. By increasing the population and the complexity of the goals and efforts of the human community to advance economic, cultural, social and political goals, we inevitably need partnership and cooperation. Participation means using personal resources to participate in a collective action. The first step is to increase popular participation in desertification initiatives, identify and remove barriers to effective non-participation in project implementation. The main objective of this research is to prioritize the factors affecting the lack of public participation in desertification plans using the FUZZY-AHP method and the Friedman nonparametric test. 

The statistical population of the study consisted of households in Ein-e-Khosh village of Dehloran Ilam and experts of Ilam University and natural resources organization of Ilam province and Dehloran County with more than ten years’ experience in combating desertification issues. In this research, the indexes and sub-indicators related to library studies, questionaire from experts of the university, experts from the Natural Resources Department of Ilam province and Dehloran city, as well as referring to the region and interviews with the residents of the region were identified. Then, the questionnaire designed by the FAHP method evaluated by inconsistency rate and its validity and reliability by Likert scale, and finally tried to prioritizing them based on the following steps. First, the prioritization of the indicators was performed from the expert's point of view using Fuzzy Analytical Hierarchy Process (FAHP). Then, the prioritization of the indicators from the perspective of experts by application of Friedman test and finally, the priority of indices and sub-indicators by the local point of view with Friedman's test. 

The findings of the research showed that the ranking of indices using Friedman's nonparametric test is based on the average rating from the viewpoint of residents of the region, respectively, economic index, design-executive, educational-promotional, and social. Also, this prioritization from the perspective of experts using the FUZZY-AHP test is design-executive, economic, educational, promotional and social priority, respectively. Also, the results showed that in the total of 15 identified subcategories influencing the lack of public participation in combating desertification projects, from the perspective of experts, the sub-design of the design-executive entitled "Short-term, mid-term and long-term non-planning for participation" with the average rating of 11.68 was the most important and the sub-index, "Migration of youth in the countryside" with the average rating of 3.59, is the most insignificant sub-indicator. However, from the perspective of residents in the region, the underlying economic indexes "disregarding people's income as a direct incentive to implement combating desertification projects" with an average of 11.24, the most significant and sub-indicator of the design-executive "lack of full allocation of funds during the implementation of combating desertification projects" with average rating of 5.63 is the most significant sub-indicator, which indicates that economic indicators and design-executive, along with the sub-indexes, are the most important reasons for non-participation of people in combating desertification projects in the study area.Due to the fact that the indicators and sub-indicators are identified based on the opinions of experts and locals in the study area, this has led to familiarizing the respondents with the research. In this research, the FAHP and Friedman test were used. According to the topic of the research in the field of public participation, the best tool for measuring the comprehensive statistical view of experts including experts and locals with regard to the study area is considered. In the reliability of the FAHP questionnaire, the responsiveness questionnaire has high reliability with regard to the multi-stage and multi-stage couples comparing method and the incompatibility rate test (mean 0.043 inconsistency rate). Cronbach's alpha test was used for Likert scale questionnaires. Results (Cronbach's alpha = 0.83) showed that the questionnaire had acceptable reliability. So, results is in consistence with other researchers' findings, including Saleh Pourjem et al. (2017). 

According to the results obtained from prioritization, it has been shown that in the subject of participation, in spite of the difference between the views of experts and the people of the region, in some cases, the main priorities in the discussion of non-participation are almost similar; these results are consistent with the studies of previous researchers such as Saleh Pourjem et al. (2017).It is suggested that the removal of obstacles to public participation in combating desertification projects be put on the agenda of trusteeship organizations and public participation in all stages of design, implementation and future protection in the combating desertification projects will be considered.
 

Complexity of natural processes especially tectonic processes that shape landscapes cannot be evaluated by classic geometry. In comparison with integer dimension of Euclidean space, fractal geometry can analyze features with non-integer dimension (Turcotte, 1977:121). Fractal behavior in such features shows self-similarity that this component is independent of the accuracy of investigation (Baas, 2002, 311). In fact, fractal dimension, is scale-invariant (Phillips, 2002, 144). Spatial variations of fractal parameters are an important factor in studying the tectonic state of regions. By determining the fractal dimension of Linear structures such as faults, it is possible to compare their geometry disorder (Suk moon et al, 1996:5). This parameter affects seismic behavior of fault because earthquake is an event related to faulting (Bachmanov, et al, 2012: 221) and Their concentration in an area indicates tectonic activity. In this research we performed fractal analysis using box counting method on fault and seismic data of northwest of Zagros about different scales of fault and different time periods of earthquake epicenters of two organizations with various detail to find and examine their fractal behavior by fractal dimension.

Data in this research can be divided to three clusters: 1. Fault lines of two scales of geology maps (1:100000 and 1:250000), 2. Earthquake epicenters of two periods of times prepared by two organizations (20 century data of Institute of Geophysics and 1900-2020 data of International Institute of Earthquake Engineering and Seismology) and 3. The second cluster with exert of Magnitude of completeness of earthquakes that show the minimum magnitude above which the data in the earthquake catalog is complete. Fractal analysis applied on these data by box counting method. To achieve this goal firstly, under study area divided to 6 boxes that contain main fault trends horizontally and vertically (A: folded Zagros in west of Kermanshah, B: faulted Zagros around Kermansha and east of kermansha, C: folded Zagros near mountain front fault, D: An area between faulted and folded Zagros near Khoramabad, E: Area around Balarud fault and F: An area between Balarud and mountain front fault to faulted Zagros). To calculate fractal dimension of fault lines and distribution of earthquake epicenters, box counting method suggested by Turcotte (1997) were applied by using Hausdorff dimension, which in two quantity of size (side length of grids) and number (number of grid boxes containing earthquake epicenter or fault) are used to calculate FD (total fractal dimension) value (Schuller et al, 2001: 3). Relation between reciprocal of side length (quantity of size) and number of boxes containing point and linear features (quantity of Number) was drawn Logarithmically as a linear regression in Excel that shows fractal dimension.

Larger values of fractal dimension indicate greater geometric disorder (Sukmono et al., 1996: 5). Analysis of faults of two scales represent that faults geometry is fractal and the amount of FD for scale of 1:100000 compared with scale of 1:250,000 is larger but their result approximately is same. The FD values for both scales are locate between 1 and 2 that expresses development of the fractal community of faults has a linear trend. On the other hand, for earthquakes, increase in FD values shows that earthquakes are not clustered and are distributed homogeneously (Oncel & Wilson, 2002: 339) along a line in understudy area. Calculated number-size values for faults and earthquakes represent both partial and popular FD changes. Based on partial FD, two populations can be classified: (a) Background with FD larger than popular FD; (b) Threshold with FD lower than popular FD.

Fractal analysis of faults of two scales of geology maps reveals that the order of active areas with high FD values in both scales are same but due to different details of faults in geology maps of geology survey and oil company, in scale of 1:100000 area labeled B and in scales of 1:250000 area labeled A is the most tectonically active region, however, area labeled E in both scales has lowest value. The order of active areas based on FD values for earthquake epicenters of 1900-2021 data of geophysics institute do not support other results because area labeled C with low density of faults and earthquake epicenters is in the first order and area labeled A is on the contrary of it. However, FD results of 20 century earthquake epicenters with exert of magnitude of completeness are reliable and higher magnitude of earthquakes spatially recent Ezgeleh earthquake in area labeled A is its evidence.

Today, poor air quality is one of the most important environmental problems in many cities around the world. Air pollution can have a devastating effect on humans, plants, organisms, and human assets, and efforts are being made to anticipate and analyze the amount of distribution and transmission of air pollutants in order to minimize the adverse effects on air quality and climate. Among the most important air pollutants are (CO), (SO2), (NO2), (O3) and aerosols (AI). Numerous studies have been conducted on the monitoring of these pollutants based on information and statistics from pollution monitoring devices, but the use of satellite images in the field of monitoring and measuring pollutants has been limited. Due to the increasing growth of these pollutants, in this study, an attempt has been made to identify the average spatial concentration of the most important air pollutants as the actual sources of pollution on the scale of Iran from October 2018 to December 2019. Also, identifying the most polluted centers in Iran based on the average of 5 pollutants is another goal of this study. Therefore, the aim of this study is to demonstrate the ability of Sentinel satellite to monitor air pollutants, and the ability of GPW images to produce a population density map for the first time on an Iranian scale.

 Using the Python programming language in the Google Earth Engine program environment, various products related to CO, SO2, NO2, O3 and AI pollutant images, obtained from Sentinel-5 satellite images during the study period and in the scale of Iran, were obtained for monitoring of air pollutants and determination of pollutants focuses. The output variable is defined as a set of images based on the time filter (2019) and the spatial filter (Iran borders). The output of the average concentration of pollutants for each month is calculated separately and annually in these filters. Then, the spatial map of the average concentration of pollutants in the Arc map software was analyzed and statistical information related to the average concentration of these pollutants was processed by SPSS statistical software. To determine the hotspots in terms of all pollutants, the raster location map of each pollutant was classified using the Jenks algorithm. In order to identify the share of provinces and counties, the amount of pollutants was also analyzed by spatial statistics in GIS environment and using the Zonal Statistics command based on the defined administrative boundaries. The G statistic was used for Cluster analysis, and in order to identify Hot Spots and Cold Spots, Getis-Ord Gi statistic (Gi) was used in GIS environment.To determine the population of each province, the latest census information of Iran as well as satellite images related to the fourth version of Gridded Population of World (GPW) product were used. Finally, The Moran index was used to determine the pattern of pollutants distribution and the spatial autocorrelation.

 Spatial output from the processing of Sentinel-5 satellite images during the study period for identifying air pollution centers in Iran showed that the highest levels of nitrogen dioxide were recorded in the majority of cities in Tehran and Alborz provinces and then in the centers of other provinces. In the case of carbon monoxide, the highest rate is in Tehran and the coasts of the Caspian Sea and Khuzestan, and coastal areas of Bushehr and Hormozgan provinces. The highest amount of ozone is in the northern parts of the provinces of West and East Azerbaijan, Ardabil, Gilan, Mazandaran, Golestan and Northern Khorasan. Most of the dust was in the southern, eastern, southeastern and central provinces of Iran. The highest amount of sulfur dioxide pollutants is recorded in Tehran and then in the provinces of Khuzestan, Kerman, Hormozgan, Bushehr, Markazi, Qom, Isfahan and Khorasan Razavi. Provincially, the highest share of nitrogen dioxide is in the provinces of Tehran, Alborz, Qazvin and Qom. The highest provincial share of carbon monoxide is in Khuzestan, Gilan and Mazandaran provinces. The highest share of dust is in the southeastern provinces, including Sistan and Baluchestan, the highest share of sulfur dioxide is in Khuzestan province, and the highest share of ozone pollution is in the coastal provinces of Caspian Sea. Compliance of the average 5 pollutants with Google Earth images showed that the contaminated areas are located in the cities of Abadan, Imam Khomeini Port, Mahshahr Port and Ahvaz (Khuzestan Province), Tehran, Pakdasht (Tehran Province) and Assaluyeh Port (Bushehr Province). The results of comparing the average concentrations of pollutants in different seasons showed that there was no significant difference between CO, NO2 and O3 pollutants in different seasons, but suspended particles and aerosols in winter and autumn seasons have a significant difference with the amount of this pollutant in spring and autumn. Also, SO2 pollutant in autumn had lower concentrations than other seasons. The results of clustering analysis to determine the status of significant spatial clusters showed that the data are in the confidence range and have spatial auto-correlation and cluster distribution pattern.

 According to Sentinel-5 satellite images, most of the pollution centers in Iran are related to petrochemical industries and refineries, which are located in the cities of Abadan, Imam Khomeini port, Mahshahr port and Ahvaz (Khuzestan province), Assaluyeh port (Bushehr province) and common pollutants. By these centers are NOX, SO2, CO, suspended particles and aerosols. Also, other centers (Tehran, Pakdasht in Tehran province) are located in the most populous urban areas of, which have been identified as hotspots in high production of NO2 and CO, due to high population and urban traffic.  Due to the higher population density of Tehran and Pakdasht than other cities in Iran, air pollution can be more important in these cities. Therefore, the use of satellite imagery to monitor Iran's air pollutants and the location of hotspots can be very cost-effective and time-consuming.
 

Drought is a temporary disorder whose characteristics vary from region to region, therefore, it is not possible to define a complete and absolute definition of drought. Drought is one of the most important natural disasters that can occur in any climate regime. Since drought is unavoidable, it is important to know it in order to optimally manage water resources. Drought prediction can play an important role in managing this phenomenon. In other words, recognizing and predicting this phenomenon is one of the topics of interest for scientists who are interested in solving the problem of water shortage. More than 80% of Iran's area is covered by arid and semi-arid regions and lack of rain is a predominant phenomenon in this region. So far, several methods have been proposed to predict drought. Each method offers different results in specific conditions.  Therefore, identifying the best method for predicting drought in the climatic conditions of central Iran is essential. 

In this research, in order to introduce a suitable method for predicting drought for the next month, four methods of artificial intelligence including Deeplearning (using the Alexnet network, one of the convoluted networks), K nearest neighbor algorithm (KNN), multi-class Support vector machines (SVM-MultiClass) and decision tree have been used. Monthly rainfall data from 11 syntactic stations of Yazd province during the 29-year statistical period (1988 to 2017) were used as experimental data. Standardized precipitation index (SPI) was calculated to indicate drought status in terms of severity and duration on 1, 3, 6, 9, 12 and 24 month time scales. Precipitation data was used as neural network input and SPI classification as network output and 80 percent of the data was used for training and 20 percent for testing the networks.In this study, the Recurrence Plot method was used to interpret the time series to convert these series into images and RG and B pages were created. To convert rainfall data into photos at 1, 3, 6, 9, 12 and 24 month time scales, photo layers were created using a standardized rainfall formula, and by merging these three output layers into colored photos and black and white photos were obtained. Using three pages created in MATLAB software and merging them, the output was in the form of a photo, which was placed as the input of the Alexnet network. Combination of Recurrence Plot to create images and deep learning network for classification of drought data has been used for the first time in this research. To evaluate the effectiveness of the classification strategy, standard criteria of accuracy, micro-F1 and macro-F1 were used.

The results showed that all networks were able to predict drought. However, on short time scales such as 3 and 9 months, the accuracy assessment criteria for some classes are zero and the methods of learning from these classes are practically ignored due to their lack of data. But on a larger time scale, this issue has been addressed and the data of those classes are well categorized. Deep learning network with image input could not predict well in the short term due to lack of data, but in the long term due to increased data has improved its performance and had the best performance. The SVM method at different time scales has shown unreliable and variable behaviors that can not be said to be a suitable method for predicting drought at different time scales. Decision Tree and KNN methods have been able to predict drought better in the short term than in the long term. The two methods have been closely related. .Based on the Deeplearning network macro-f1 evaluation criterion, the one-month time scale with 22.71% was the most inefficient method and the Decision Tree with 64.65% was the most efficient method, But with the increase in time scale, the Deeplearning network improved its performance, so that at the 24-month time scale with 65.35%, the best performance for the Deeplearning network followed by the SVM-MultiClass network with 57.40%. For future research, it is suggested that Decision Tree and KNN methods be used to predict short-term drought. In this study, with increasing the time scale and increasing the data used, these two methods have lost their effectiveness compared to the short term.
 

Full identification of hazards and prioritizing them for non-harm to nature is one of the first steps in natural resource management. Therefore, introducing a comprehensive system of evaluation, understanding, and evaluation is essential for controlling hazards. This study aimed to model and predict environmental hazards following increased degradation in natural environments by ANN. Thus, 600 soil and vegetation samples were collected from inhomogeneous ecological units. Soil samples were prepared by strip transect method according to soil depth in four profiles (5, 10, 15, 20 cm). Vegetation samples were also collected using a minimum level method using 2 2 square plots according to the type, density, and distribution of vegetation. Sampling was done in two safe zones and other uses were modeled using ANN in MATLAB environment. The optimal model of multilayer perceptron with two hidden layers, sigmoid tangent function and 19 neurons per layer and coefficient of determination of 0.90. The results of sensitivity analysis showed that soil moisture content would be effective in decreasing biodiversity and flood risk as well as increasing the risk of extinction of endemic species in the region, and then the apparent and true gravity and soil porosity and distance from the road play a key role in the degradation of cover. Vegetation has increased flooding and extinction risk. Therefore, it is recommended that measures related to soil and vegetation restoration in this park be taken to reduce future damages as soon as possible.
 

Today, the expansion of human societies and greater environmental dominance have led to faster and wider environmental change than ever before. The speed and variety of this change in urban environments is greater than in other areas. The purpose of this study was to investigate the temporal and spatial variability of four coastal cities of Khuzestan province (Bandar Imam Khomeini, Bandar Mahshahr, Abadan and Khorramshahr) using land use measures over a period of 20 years 1997-2009 to accurately determine spatial-temporal pattern of changes. is. The method of the present research is quantitative and its dominance is dichotomous. To extract the land cover map data through Landsat satellite imagery from 1977 and 1998 taken by OLI and MSS5 sensors, the images were divided into four main classes (residential), vegetated areas, wetlands (rivers). And Bayer were categorized. After preparing land cover maps from TerrSat software was used to analyze land use changes and finally using the Markov chain to predict urban development trend in the study areas. The results show that Abadan and Khorramshahr have the most changes in vegetation use, while in the two cities of Imam Khomeini (Rah) and Mahshahr the most changes were related to the use of Bayer. Added to the timeline.