نشریه تحلیل فضایی مخاطرات محیطی
سال دهم شماره 3 (پاییز 1402)

This paper is a discussion of urban heat islands (UHIs), which unique residential areas are characterized by dense central cores surrounded by less dense peripheral lands. UHIs experience higher temperatures due to impermeable surfaces and specific land use patterns. These temperature variations have negative environmental and social impacts, leading to increased energy consumption, air pollution, and public health concerns. It emphasizes the need for simpler approaches to comprehend UHI temperature dynamics and explains how urban development patterns contribute to land surface temperature variation. The case study of Guilan Plain illustrates the relationship between development patterns and temperature, utilizing techniques like principal component analysis and generalized additive models.
This paper focuses on mapping land use and land surface temperature in the southwestern region of the Caspian Sea, specifically in the low-lying area of Guilan province. The research utilized satellite data from Landsat sensors for three different time periods: 2002, 2012, and 2021. A spatial unit known as a "city block" was employed through object-based analysis using eCognition software. Thermal bands from Landsat, such as TM band 6, ETM+ band 6, and TIR-1 band 10, were used to retrieve land surface temperature. The radiative transfer equation was used to calculate temperature, accounting for atmospheric and emissivity effects.
The study employed the normalized difference vegetation index (NDVI) method to estimate land surface radiance. The main focus of the study was to identify predictive variables for urban land surface temperature within the context of residential city blocks. These variables were categorized as intrinsic (related to the block's structure) and neighboring (related to adjacent blocks) variables. Intrinsic variables included block area, shape index, perimeter-to-area ratio, and central core index, calculated using Fragstats software. Neighboring variables encompassed metrics like shared boundary length, mother polygon area, number of neighboring blocks, average distance to neighboring block centers, average area of neighboring blocks, average shape index of neighboring blocks, and average central core index of neighboring blocks. Principal Component Analysis (PCA) was employed to select significant variables that captured the majority of data variance. Variables with eigenvalues greater than 1 in each principal component were considered significant contributors. Varimax rotation was applied to the PCA results to ensure accurate variable selection.
The study utilized a Generalized Additive Model (GAM) approach, implemented using the mgcv package in R, to model the relationship between urban land surface temperature and predictor variables. Smoothing parameters were estimated using a restricted maximum likelihood method. Model accuracy and interpretability were assessed using the coefficient of determination (R-squared) and the F-test analysis. the study's results include the generation of land use maps for three different time periods using object-based image analysis. Urban block characteristics were aligned with spectral units through density, shape, and scale coefficients. Over the years, the average block size showed variation, increasing from 61.19 hectares to 62.21 hectares. Urban expansion was observed across the years, with the urban area expanding from 9.5% to 11.1% of the region. Surface temperatures ranged from 22.84 to 26.26°C, with urban temperatures spanning 26.14 to 53.04°C. Independent variables were calculated for intrinsic and neighboring categories, with varying characteristics like block size, shape index, and perimeter-to-area ratio. Principal Component Analysis identified influential parameters, leading to the selection of block size, and shared boundary. the polygon area, and perimeter-to-area ratio as main variables for a generalized additive regression model. This model demonstrated non-linear relationships between these predictors and urban temperature. Block size, shared boundary, and mother polygon area exhibited a positive relationship with temperature, while the perimeter-to-area ratio displayed a negative trend. The model's performance was satisfactory, indicated by an R-squared value of 0.619.
The discussion focuses on the challenges and complexities of predicting urban surface temperature through studies on land use patterns. the current study concentrates on analyzing surface temperature within urban block units and categorizing variables into intrinsic and neighboring factors to enhance the understanding of the relationship between urban surface temperature and spatial distribution. Despite calculating urban surface temperature as a seasonal average across years, notable variations in temperatures were observed across different years. These variations are attributed to environmental conditions, climatic factors, and atmospheric influences that fluctuate over time. Consequently, the study aims to mitigate the impact of dynamic parameters by basing its models on cumulative temperature changes over various years. However, despite its reliability, this approach might lead to biased results when dealing with short-term time-series imagery.
The discussion also delves into the study's approach of focusing on spatial indices of urban units as predictive neighboring parameters. This choice stems from the fact that other units, particularly agricultural ones, experience significant changes over shorter periods, which can disrupt model calibration. Principal Component Analysis highlights the importance of block size as a key predictor of urban surface temperature, emphasizing the shift from polygon area to block size as a spatial scale. The study concludes that both block size and aggregation significantly influence urban temperature patterns. The Generalized Additive Model reveals that block size and mother polygon area exhibit a positive relationship with urban surface temperature, while the perimeter-to-area ratio displays an inverse correlation. This parameter indicates that units with smaller central cores and higher perimeter-to-area ratios experience cooler temperatures due to engagement with neighboring units, especially agricultural ones. In conclusion, the findings suggest that urban blocks function as distinct entities where temperature-related factors are influenced by intrinsic attributes like shape, as well as by the positioning of a unit relative to others.
The conclusion highlights the continuous growth of studies investigating the connection between land use patterns and urban surface temperature. Block size emerges as a central factor in determining urban surface temperature, alongside block dispersion and aggregation, which play crucial roles as predictors in residential areas. Additionally, the study emphasizes the importance of spatial configuration and unit structure in shaping urban temperature patterns. The proposed methodology has the potential to enhance understanding of parameter significance in shaping urban temperature patterns across various regions of Iran.

Extensive and massive agriculture, along with other agricultural activities such as animal husbandry, industrial activities in the southern half of the province, has created and intensified extensive changes in the environmental resources and natural structure of the province. This extensive change can show its effects and consequences in the destruction of forest lands, the transformation of rich pastures into poor pastures and barren lands, severe soil erosion, and finally the creation and development of internal centers of dust. and intensify the severity of dust incidents in the province. Dust events have profound and significant effects on agriculture and soil fertility, health and hygiene, disruption and destruction of industries and power plants, and negative effects on the environment, including the deterioration of forests. Airborne particles, which are mainly driven to the region by dust storms, are one of the important components of the atmospheric system. They can not only change the albedo of the energy balance by acting as cloud particle nuclei, or ice nuclei.

The study location of this research is Khuzestan province, which is one of the most challenging provinces in the country in terms of environmental hazards. This province, with an area of about 6.5 million hectares, occupies about 4% of the country's area. Dust is one of the major and most important challenges of this province. Its destructive effects can be traced in various dimensions, such as the quality of water resources, the quality and performance of agricultural products, industries and energy transmission networks, and the air quality of cities. Three categories of data have been used in this research. The data of the first category is related to the data of widespread dust days in Khuzestan province. These data were obtained from the dust codes of the current air condition (ww parameter of synoptic stations of the province) during the statistical period of 2000, 2020. The second category of data was actually the remote sensing data of MODIS sensor, which included the Aerosol Optical Depth (AOD) product of MODIS sensor (MOD04 product) and Aerosol Exponential Index (AEA). These two indicators are dimensionless but with different directions. In the AOD index, higher numbers represent more aerosols in the atmosphere and in the AEA index, in addition to the presence of dust in the place, it also provides the size of the aerosol particles. Finally, the third category of data is the reanalysis data related to incoming net shortwave radiation (SNSR), which was taken from the reanalysis data of the European ECMWF database version ERA5 with a spatial resolution of 0.5 arc degrees. 

In this research, it was tried to investigate the influence of the dust event in the context of fluctuations and daily changes in the amount of net shortwave radiation received on the earth's surface. The results of the investigation of three cases of widespread dust in the province showed that in these three cases of widespread dust, aerosol particles are generally in the central, southern and western parts of the province (plain and lowland areas of the province) from the type of medium to large particles (index angstrom between 0.5 and 1) and in the eastern and northeastern parts, it was of the type of coarse particles (angstrom index less than 0.5). In the context of the impact of dust events on the amount of shortwave radiation received by the earth's surface, it was seen that in the dust event of July 22, 2010, the Angstrom exponential index indicates the presence of coarse particles in the atmosphere near the earth's surface and the AOD index also indicates the presence of dense dust in the entire area of the province. The received net shortwave radiation (at 12 noon or 09 UTC) was about 194 watts per square meter (about 28 percent) lower than the average for the same month. This drop rate was less in the other two dust waves, whose AOD and Angstrom index values indicated finer and less concentrated dust. In the dust wave of June 19, 2012, the amount of net shortwave radiation received was only 5% (25 W/m2 at 12 noon or 09 UTC) less than the long-term average, and this drop in the dust event of May 12, 2018 was equal to 28 W/m square (about 4% drop compared to the average of the same month).

Agricultural activities, as a foundation of growth and development and part of the rural development process, guarantee the economic life of many villages in the country. However, in recent years, other products' water scarcity and resource limitations have affected these activities. This issue has severely challenged the sustainability and life of rural settlements.
In this regard, organizing and developing an optimal cropping pattern is necessary to achieve the goals of sustainable agricultural and rural development in Iran. To achieve this goal, the cultivation of crops must be commensurate with the capabilities of production resources, especially water resources.
Therefore, determining the appropriate spatial distribution of agricultural lands for the cultivation of various crops is one of the primary foundations for implementing optimal cropping pattern. Accordingly, the present study seeks to identify suitable spatial zoning for wheat and barley cultivation as the main crops in agricultural lands in traditional Lenjanat regions, which are exposed to a growing water crisis.

According to the main purpose of the research, the data obtained from spatial distribution maps of current cropping patterns and spatial distribution of suitable lands for crop cultivation.
This study prepared the suitability maps of the major agricultural products at a distance of 10 km on both sides of Zayandeh Rud River in Lenjanat region using multi-criteria decision-making methods.
Thus, the agronomic-ecological needs of the two major crops in the area (wheat and barley) were determined, and a standard map for each crop was classified using ArcGIS software. Then, the digital layers were combined by allocating the weight obtained from the Analytical Hierarchy Process and the Simple Additive Weighting method. Finally, talent assessment and land zoning was performed in four categories from unsuitable to very suitable for cultivating wheat and barley crops. Using the analytical hierarchy process method and experts' opinions led to high accuracy results.

The results of the land suitability map showed that 90.6% of the agricultural lands in the study area are very suitable and relatively suitable for the cultivation of the wheat crop. The northern and eastern regions, located in Falavarjan county and the north part of Mobarakeh county, are the most suitable areas for wheat cultivation. As we move from the north and east to the west of the study area, the capability areas for wheat cultivation decrease. Limiting factors in these areas are unsuitable soil texture, low temperature, shallow soil, high slope, low rainfall and drainage.
As for barley cultivation, a large part of the area, equal to 30635.3 hectares (more than 91%), is very suitable and relatively suitable. In these areas, in the northern and eastern parts of Lenjanat, unsuitable soil texture, shallow soil, high slope and low drainage are the most critical limiting factors for barley cultivation.
A comparison of "spatial distribution of land suitability" with "spatial distribution of cropping pattern" shows that the crops in this study (wheat and barley) have been cultivated in a suitable area in terms of the ecological potential of lands.

The results of this evaluation can be used in the spatial distribution of the optimal cropping pattern to select a suitable cultivation site for these two crops and other existing and alternative crops.
Wheat and barley are the major crops usually used in planning optimal cropping patterns, regardless of the economic issues. Considering suitable spatial distribution for wheat and barley, they should be distributed in such a way with the slightest difference compared to the current cropping pattern. On the other hand, a large area of the Lenjanat region is suitable for cultivating wheat and barley. In addition, an agricultural unit may have different capacities for other crops, so it is necessary to pay attention to the ecological potential of other crops. Wheat and barley should be cultivated in lands which are unsuitable or semi-suitable for other crops.
Accordingly, it is necessary to provide spatial zoning of existing and alternative crops in the Lenjanat area with fewer water requirements and higher economic benefits to be introduced in the optimal cropping pattern.
In this study, only agronomic-ecological criteria and needs with available data were examined due to data limitations in assessing crop suitability. Therefore, completing land suitability maps by considering more evaluation criteria such as evapotranspiration and the amount of water available is recommended.
Also, to have a "spatial distribution of the optimal cropping pattern", paying attention to the ecological potential of the lands, also considering other criteria and priorities such as natural, socio-cultural, economic and political criteria is necessary. So, we can develop a cropping pattern that provides a basis for desirable space dynamics.

The pervasiveness of the concept of vulnerability in various dimensions has led to the emergence of the theory of vulnerability in the spatial sciences. According to the theory of vulnerability, in any given space, there is a coefficient of vulnerability, while the levels and amplitude of safety are not evenly distributed on the surface of that space. Residential use is one of the most important and main uses in the urban land use system, and safety management and attention to its defense requirements are very important due to the high population density in large cities. The present study is in the field of assessing the vulnerability of residential uses against external threats with a passive urban defense approach in District 10 of Tehran, which was conducted in the form of spatial studies and by implementing an analytical model in three steps. First, the principles and requirements of passive defense were identified and classified into three groups of structural, demographic and spatial parameters, and using the questionnaire and expert survey tools, the priorities of passive defense principles in relation to residential spaces were determined. Then, based on the network analysis process, the weight of each criterion was determined and the weight of the ANP model was applied to the spatial layers of the region in ArcGIS software. The results of the model showed that in terms of structural indicators, more than 78% of residential units in the region are in the group of structures with high vulnerability and in terms of demographic indicators, in 88% of residential units in case of external threats, the level of vulnerability is high. In terms of spatial indicators, more than 92% of residential spaces are adjacent to several incompatible uses and have the highest vulnerability. In general, the results of overlapping layers showed that more than 86% of residential units in the area are located in vulnerable zones and the vulnerability of residential units in these zones is very high.

Desertification is one of the major environmental, socio-economic problems in many countries of the world (Breckle, et.al., 2001). Desertification is actually called land degradation in dry, semi-arid and semi-humid areas, the effects of human activities being one of  the most important factors (David and Nicholas, 1994). Sand areas are one of the desert  landforms, whose progress and development can threaten infrastructure facilities. The timely and correct identification of the changes in the earth's surface creates a basis for a better understanding of the connections and interactions between humans and natural phenomena for better management of resources. To identify land cover changes, it is possible to use multi-temporal data and quantitative analysis of these data at different times (Lu, et.al., 2004), therefore, one of the accurate management tools that causes the application of management based on current knowledge, these studies Monitoring is done using the mentioned data. The use of satellite data and ground information in such studies has caused many temporal and spatial changes of phenomena to be well depicted, which can be beneficial in better understanding  and  interaction with the environment and ultimately its sustainable management  and development. To obtain and extract basic information, the best tool is to use telemetry technologies, which by using satellite data, in addition to reducing costs, increases accuracy and speed, and its importance is increasing day by day in the direction of sustainable development (Alavi Panah, 1385). Since field studies in the field of spatial changes of sandy areas of this city are difficult and expensive to repeat, facilities such as simulating these areas with expert algorithms and artificial intelligence can be used to investigate and monitor critical areas at regular intervals. Accurate and economically appropriate. Therefore, in this research, with the aim of investigating the effectiveness of these models in the periodic changes of the sandy plains of Ferkhes plain, two algorithms, perceptron neural network and random forest, were chosen, and the reason for choosing these models is the ability to model according to the existing uncertainties, interference Fewer users and insensitivity of the model to how the data is distributed.

The progress and development of the sandy areas of the Fern Plain depends on three factors, climatic, environmental and human. Therefore, the input variables to the expert and artificial intelligence models were chosen to cover these three factors. Therefore, factors such as drought, the number of dusty days, as well as vegetation index were entered into the model as dynamic variables, and environmental factors such as soil, elevation and altitude, geology, slope and direction were entered into the model as static variables. The statistical period investigated for the changes of wind erosion zones was considered to be 15 years from 2000 to 2015, based on this time base, qualitatively homogeneous and reconstructed meteorological data and images A satellite was selected and processed in 5-year periods (2000, 2005, 2010 and 2015). Modeling of the changes of sandy areas was done using two algorithms of perceptron neural network and random forest in MATLAB software environment. To choose the best neural network structure, a large number of neural networks with different structures were designed and evaluated. These neural networks were built and implemented by changing adjustable parameters (including transfer function, learning rule, number of middle layer, number of neurons of middle layer, number of patterns). One of the most common types of neural networks is multilayer perceptron (MLP). This network consists of an input layer, one or more hidden layers and an output. MLP can be trained by a back propagation algorithm. Typically, MLP is organized as a set of interconnected layers of input, hidden, and output artificial. The accuracy of these networks was checked by the statistical criteria calculated in the test stage, and finally the network that had the closest result to the reality was selected as the main network. The main active function used in this research is sigmoid, which is a logistic function. Then by comparing the network output and the actual output, the error value is calculated, this error is returned in the form of back propagation (BP) in the network to reset the connecting weights of the nodes (Chang and Liao, 2012). Other evaluation indices MSE, RMSE and R were used as network performance criteria in training and validation. The selection of Fern plain as a study area is due to the high potential of this area in the advancement of sand areas, for this purpose, 8 effective factors in the development of these areas were investigated. These factors were entered into the model in the form of three dynamic indices and five static indices.

In evaluating the results of modeling algorithms, dynamic variables in all periods were introduced as the most important factors in the occurrence of wind erosion and the advancement of sand areas. The diagram of the importance of predictor variables is presented in Figure 7. The results show that the vegetation cover index ranks first in all periods, the drought index ranks second in 2000 and 2015, and the dust days index ranks third in these two years. Meanwhile, in 2005 and 2010, the dust index and drought index ranked second and third respectively. Among the static variables used in this research, the height digital model variable was ranked fourth in 2000 and 2010, and in 2005 and 2015, geological and soil variables were important. In almost all studied periods, the direction factor is less important than other factors, which can be removed from the set of variables required for modeling to predict sand areas.

Land subsidence is one of the environmental hazards that threatens most countries today, including the majority of Iran's plains (Ranjabr and Jafari, 2010). Damages caused by subsidence can be direct or indirect. Infrastructural effects are direct and indirect effects of subsidence, but economic, social and environmental effects are indirect effects of subsidence (Bucx, et al., 2015). The environmental effects of subsidence are related to other effects of subsidence, including the infrastructural, economic and social effects of subsidence. The southwest plain of Tehran is considered one of the most important plains of Iran due to its large areas of residential, agricultural and industrial lands from various aspects, especially economic, political and social. The subsidence of the Tehran plain was first noticed by the measurements of the country's mapping organization in the 1370s. Since 2004, the responsibility of investigating this phenomenon in the plains of Tehran was entrusted to the Organization of Geology and Mineral Explorations of the country. Although several researches have been done in the field of subsidence factors, amount and zoning. In the field of estimation of subsidence and changes in water level, spatial correlation of subsidence with changes in water level and estimation of vulnerability due to subsidence according to the density of population, settlements and facilities in the southwestern plain of Tehran has not been done.

In the current research, we will analyze and estimate the spatial regression of the subsidence phenomenon by InSAR technique with water level changes from 2005 to 2017, as well as the environmental effects of subsidence in the southwest plain of Tehran by using Quadratic analysis method (O’Sullivan and Unwin, 2010). The criteria map of the current research is overlapped using the ANP method (Ahmedabadi and Ghasemi, 2015) weighting and finally with the SAW method (Emaduddin et al., 2014) in the Arc GIS 10.8 software, and the vulnerability map due to land subsidence in the study area is prepared.

The average subsidence in 12 years is about 9.9 cm per year. Average subsidence has occurred in four main zones. Maximum and minimum subsidence have been observed in B (near the Sabashahr) and D (in east of plain) zones respectively. The results of the interpolation of the depth of the underground water in the study area indicate that the general trend of increasing the depth from the south (10 meter) to the north (more than 90 meter) of the plain. The results of spatial correlation showed that there is a significant direct relationship between the spatial layer of the average subsidence rate of Tehran Plain and the spatial data of the underground water level, and the R value is equal to 0.61. The distribution map of the underground water depth of the study area in the form of Quadrat analysis shows that in the main part of the plain, the depth of underground water is at an average level. The general trend of changes in the level of underground water is decreasing from northwest to southeast and is in 5 levels. The distribution of the networks shows that the rivers have three linear trends from north and northwest to south; their dispersion is mostly in the center of the study area. The flood rate is higher in the central plain networks. In study area, there are important arterial roads such as Tehran-Qom highway, Tehran-Saveh highway and Tehran Azadegan highway. The southern and northeastern areas of the study area are urban settlements such as Islamshahr, the 18th and 19th districts of Tehran Municipality and other residential areas such as Sabashahr. The major part of the region has fertile soil and the occurrence of subsidence can have negative effects on the fertility and texture of the soil in the study area. The results of vulnerability analysis due to subsidence show that there are 5 vulnerability classes in the study area including very low, low, medium, high and very high.

All in all most of the study areas (central, northern and western networks) are in medium, high and very high vulnerability. About 14,600 hectares of the study area are in medium vulnerability. Which is continuous from the west to the east of the study area. Most of the urban infrastructures are moderately vulnerable to subsidence. About 17,000 hectares of the southwestern plain of Tehran are very vulnerable. That more than half of the area of ​​this area is covered by settlements and urban infrastructures. Therefore, the phenomenon of subsidence causes irreparable damage to the settlements and infrastructures in the southwest plain.

Measuring urban resilience can help develop appropriate strategies and policies for cities facing unexpected shocks and their consequences. Since urban resilience is a complex concept and difficult to operationalize, developing a technique or method to actualize this concept is a major milestone in understanding the factors and interactions that help create and maintain resilience. Tehran's metropolis has a high concentration of industries, government organizations, services, and facilities, which makes its management very complicated when a natural disaster occurs. Previous conditions or inherent socio-economic characteristics show that Tehran is not immune from flood forces. In fact, it is important to measure resilience against urban disasters for areas located on rivers in Tehran due to its inherent characteristics and spatial-temporal changes of floods in the region. This research focuses on measuring the resilience of the areas located on the rivers of Tehran. The measurement approach is based on creating a composite index based on six dimensions of social, economic, institutional, infrastructure, social capital, and environmental resilience against floods. This research has been done by developing a mixed multi-criteria decision-making method. The AHP model has been used for prioritizing the selected indicators and the TOPSIS model has been used to rank the areas located on the rivers of Tehran city based on their resilience levels. The results show that region 22 is the most resilient region, while regions 4, 5, and 14 have the lowest resilience levels. The findings of this research can help urban planning organizations such as Tehran Research Planning Center to integrate disaster resilience in urban planning and change from reactive plans to preventive urban adaptive strategies such as risk-sensitive urban land use planning.

Every year, natural hazards occur with great severity and sometimes they destroy people completely Today, science has proven that natural hazards cannot be avoided. He simply considered a natural event and did not pay attention to their complex causes. Most of these causes are attributed to a combination of socio-economic factors. But it is possible to reduce their consequences by carefully planning against such accidents. When these hazards and disasters have a human aspect and affect humans, human activity and human environment, they are introduced as crisis.
According to the statistics of the Food and Agriculture Organization of FAO, 5-15% of agricultural products are lost annually due to damage caused by frost and frost, this number reaches more than 40% for some sensitive garden products, especially almonds, pistachios and apricots. . The amount of damage caused by this complication in Iran is more than 500 million dollars. Rural settlements suffer the most damage after a drought. Thus, there is a significant relationship between the risk management of agricultural activities with most environmental components and natural disasters such as drought, flood, frost, etc. up to the 99% confidence level. In order to reduce the effects of natural hazards in rural areas, there are various strategies that can be used to manage the risk of natural hazards , diversification of agricultural productio, contract farming and increasing farmers' awareness of natural hazards.
Gardens are one of the most important sources of livelihood in rural areas in Tuysarkan city in Hamadan province. Tuysarkan city has 7600 hectares of garden lands, which includes 14% of all gardens in Hamadan province. Due to its geographical location, weather conditions and geological structure, this city is exposed to various natural hazards. Among them, we can mention drought, land subsidence, frost and earthquake. Identifying natural hazards in Tuiserkan city and the effects of these hazards, as well as the actions of the local community to reduce existing hazards, are among the most important goals of this research.

In the current research, we tried to use different methods so that the subject can be better investigated from different angles of research. This research is applied in terms of purpose and based on a descriptive-analytical research plan and is considered analytical-exploratory in nature. The research data has been collected through questionnaires and official statistics of institutions such as Iran Water Resources Management Company and the country's Meteorological Organization. Data analysis has been done descriptively and analytically using Excel and GIS software.

The results of the research show that the most important hazards in the field of horticulture in the central part of Tuiserkan are frost in the first place and drought and hail in the second and third places. Also, other results show that the most important risk that affects the livelihood and income of the local community is the annual frost of gardens, which has caused the migration of some family members, and the amount of income is also affected by this risk. Regarding the solutions proposed by the local community to reduce the effects of natural hazards on walnut orchards, providing financial facilities, using information technology, and planting cold-resistant species were among the most important solutions proposed by the local community. Regarding the analysis of open questions and conducted interviews, Netaj shows that the most important measures to reduce the effects of natural hazards (freezing, drought and hail) on walnut orchards are: heating the orchard environment, using resistant and using drip irrigation. Also, the evaluation of the analysis of local knowledge and the experience of the past regarding measures to reduce the effects of frost on walnut orchards shows that the actions of the past are not very popular with the current generation and they are doing the same thing that the past did. With this difference, the ancients believed more in luck and destiny than in practical action. Finally, from the point of view of the local community, the best measure to reduce the effects of frost on the walnut orchards in the central part of Tuiserkan is genetic modification of the orchards and cultivation of resistant species.

This study was aimed at examining the types of inversion and their severity using the thermodynamic indices of the atmosphere such as SI, LI, KI and TT at Bandar Abbas Station for 2010-2020. In this study, Radioosvand data at the Bandar Abbas Station was obtained and used from the University of Wioming for the last 11 years (3.5 local) during the last 11 years (2010 to 2020). The results of the analysis showed that the average number of inversion phenomenon in Bandar Abbas was 501 cases per year, as in some days several types of inversion were observed at different altitude. Of these inversion, about 31.6 % are related to radiation temperature inversion, 4.3 % front, and another 64.1 % for subsidence inversion. Due to the air session underneath, the share of subsidence inversions is more than other types of inversion. In the meantime, the most severe inversion of subsidence was 1354 and the weakest inversions were with 29 cases and fronts. In general, the long -term average intensity coefficient of inversion of Bandar Abbas station with a coefficient of 0.062 indicates that the intensity of the city's inversion is mostly extremely severe, which can be very destructive effects both environmentally and physical health in the city's residents. Bandar Abbas follow. The correlation between the inversion elements also showed that by reducing the thickness of the inversion layer, the intensity of temperature inversion also increased.

In recent years, groundwater discharge is more than recharge, resulting in a drop-down in groundwater levels. Rangeland and forest are considered the main recharge areas of groundwater, while the most uses of these resources are done in agricultural areas. The main goal of this research is to use machine learning algorithms including random forest and Shannon's entropy function to model groundwater resources in a semi-arid rangeland in western Iran. Therefore, the layers of slope degree, slope aspect, elevation, distance from the fault, the shape of the slope, distance from the waterway, distance from the road, rainfall, lithology, and land use were prepared. After determining the weight of the parameters using Shannon's entropy function and then determining their classes, the final map of the areas with the potential of groundwater resources was modeled from the combination of the weight of the parameters and their classes. In addition, R 3.5.1 software and the randomForest package were used to run the random forest (RF) model. In this research, k-fold cross-validation was used to validate the models. Moreover, the statistical indices of MAE, RMSE, and R2 were used to evaluate the efficiency of the RF model and Shannon's entropy for finding the potential of underground water resources. The results showed that the RF model with accuracy (RMSE: 3.41, MAE: 2.85, R² = 0.825) has higher accuracy than Shannon's entropy model with accuracy (R² = 0.727, RMSE: 4.36, MAE: 3.34). The findings of the random forest model showed that most of the studied area has medium potential (26954.2 ha) and a very small area (205.61 ha) has no groundwater potential. On the other hand, the results of Shannon's entropy model showed that most of the studied area has medium potential (24633.05 ha) and a very small area (1502.1 ha) has no groundwater potential.

Nowadays, due to global warming, drought and the occurrence of cold periods and heat stress, the study of climatic variables is very important. Therefore, in this research, the long-term forecast of temperature changes in northwest Iran in the base period (1985-2014) and three periods of the near future (2021-2050), the medium future (2051-2080) and the distant future (2100- 2081) was paid. For this purpose, 2 extreme temperature indices including Warm spells duration index (WSDI) and cold spells duration index (CSDI) and Maan-Kendall trend test were used to check the changes. To predict the changes of the profiles in the future period after evaluating 7 general circulation models (GCMs) from the sixth report model series (CMIP6) from two optimal models under three socio-economic forcing scenarios including SSP1-2.6, SSP3-7.0 and SSP5-8.5 was used. The spatial distribution of the trend of changes in the Warm spells duration index (WSDI) in the base period showed that its maximum core is located in the south and southwest of the region, and its amount decreases by moving towards the north and northeast. Spatial changes of the Cold spells duration index (CSDI) are characterized by its maximum cores in the western regions and around Lake Urmia and minimum cores in the central and northern regions of the study area. According to the results, the average Warm spells duration index (WSDI) and of the Cold spells duration index (CSDI) are equal to 5.53 and 3.80 days per year, respectively, and the maximum and minimum Warm spells duration index (WSDI) are 1.8 and 2.7 days, respectively Piranshahr and Parsabad stations and the maximum and minimum and the Cold spells duration index (CSDI) are also 5.7 and 1.32 days corresponding to Zarineh and Marivan stations. Examining the trend of changes also showed that in most stations, the WSDI index has an increasing trend, and this trend has become significant in some stations, but the CSDI index has a decreasing trend and is not significant in any of the stations. The evaluation of different models with different error measurement indices also showed that MRI-ESM2-0 and MPI-ESM1-2-L models have the best performance in simulating temperature extreme in the studied area. The distribution of changes in the future period also showed that the WSDI will increase in most stations and based on all three scenarios, especially the SSP5-8.5 scenario, but the CSDI trend will decrease in most stations and based on the SSP3-7.0 and SSP5-8.5 scenarios will be significant.

Providing food to achieve food security is considered one of the important goals of development in all countries, reducing food insecurity is considered an important political and social achievement for governments. One of the effects of food insecurity in rural areas is the number of patients and deaths caused by the corona epidemic. In this regard, the current research seeks to answer the following questions: What is the level of food insecurity in the studied villages? And what effect does food insecurity have on the spatial distribution of corona patients and deaths? The type of research is applied and descriptive-analytical in nature. The statistical population of this research is all the villages in the central part of Zanjan city, which were surveyed in full. The method of collecting information is in the form of a library and the method of data analysis is in the form of descriptive statistics and spatial analysis. The findings of the research show that the average food insecurity of the studied villages is equal to 36.08%, the highest level of food insecurity is related to Taham district with 40.76% and the lowest level of food insecurity is related to Mirizat district. In order to analyze the effects of food insecurity on the mortality caused by Corona, geographic weighted regression has been used, based on the results obtained from this spatial analysis tool, the width is equal to 0.172, the remaining squares are equal to 2836, the effective number is equal to 16.86, Sigma is equal to 5.64 and the coefficient of determination, which measures the degree of linear relationship between two variables, has been calculated as equal to 0.72, so it can be said that with the increase in food insecurity, the death rate due to Corona will also increase.