فهرست مطالب خلیل ولیزاده کامران

In addition to natural disasters such as floods, which cause great damage to the environment and human societies, the phenomenon of dust also causes irreparable damage to urban environments, transportation systems, respiratory systems, etc. Identifying Potential Dust Source Areas is considered the first step to control and prevent this phenomenon. Various researches have been conducted to identify dust sources, but most studies have relied on small-scale images. This research aims to use medium-scale satellite images to identify local areas prone to dust production. Google Earth Engine was used to analyze factors influencing dust generation, including slope, Digital Elevation Model (DEM), land use, soil salinity, Normalized Difference Vegetation Index (NDVI), soil moisture, wind speed, precipitation, and Land Surface Temperature (LST). Analytical Hierarchy Process (AHP) was used to assign weights to these elements. The resulting weights were: soil moisture (0.264), NDVI (0.208), wind speed (0.153), precipitation (0.107), land use/soil salinity (0.081), LST (0.064), DEM (0.024), and slope (0.020). The inconsistency index (0.015) indicated a high degree of consistency between the assigned weights, which is below the acceptable threshold (1). Due to the lack of ground-based air quality measurements, the Aerosol Optical Depth product (a satellite-derived measurement of airborne particles) was used to validate the resulting dust source maps. The final map showed that the potential for dust generation decreased closer to the heights of Sahand and increased closer to the lake.

One of the hazards that threatens the existing infrastructures in different regions is the phenomenon of landslides. The present study tries to identify the prone areas of this natural phenomenon in Khalkhal city, which was done using the neural network method. For this purpose, 9 factors affecting landslides were identified and prepared, the layer of landslides that happened, obtained from aerial photos and satellite images and field visits, and using non-sliding points in the region, perceptron neural network training data. They created several layers. In order to model the neural network, these data were transferred to the MATLAB 2016 software after initial preparation in the ARC GIS 10.5 software environment and were trained using MLP neural network coding to deal with the data that they have not encountered. , make predictions. The structure of the designed neural network was selected from among the many networks that were created and tested, 1-12-9, which obtained 9 inputs as the number of effective criteria, 12 neurons in the middle layer and one neuron and layer for the output of the network. The results of the validation chart of the neural network (ROC) model show a high accuracy of 95% of the created model in predicting sliding pixels. According to the results, 0.57%, 0.11%, 0.07%, 0.06% and 0.07% of the studied area were placed in very high, high, medium, low and very low classes, respectively. Due to the fact that the largest area of the region is in a very high risk class,

Road transport network is facing a wide range of natural and human disasters, because of geographical extent and functional diversity. In addition to direct damage to road transport networks, accidents can cause indirect damage to road network users and even entire community. Present research is an attempt for assessing potential areas of road accidents in highways of East Azerbaijan province. In this regard, four main criteria (climate, environmental, topographic and road factors) and fourteen sub-criteria were used. The weights of each criterion and their sub-criteria were computed using ANP model. In doing so, the final map of proposed sites for road accidents was prepared by integrating them with standard layers. The results of this study revealed that road safety criterion has the most importance in road accidents and the sub-criterion of the crash is also of paramount importance. Furthermore, the segment of Koshksaray-Marand was the most dangerous area and segment of Jolfa-Norduz was the least risky area in terms of the probability of occurrence of road accidents in highways of East Azerbaijan province.

In order to better manage natural ecosystems, man-made, long-term planning can help environmental planners and natural resource managers make more informed decisions. The purpose of this study is to identify the key factors affecting land use change in Fandolo forest area with a future research approach. At the beginning of the study, 19 factors affecting forest use changes in various economic, social, physical, natural and political dimensions were approved by experts. Dimensions 19 * 19 were designed and provided to experts for weighting from numbers 3 to 0, which were three high and zero effects without impact and weighting.The effect of variables was directly and indirectly analyzed in MICMAC software. Finally, eight effective factors in changing the forest use of Fandolo region were identified. Among the key factors of tourist factor, land use, distance from village and population, logging, vegetation, motivation to change from agricultural to residential and height are the most important key factors in the future of the regional system.

Urmia Lake is one of the largest saltwater lakes in the world, which unfortunately is drying up and has caused many dangers and concerns, especially in relation to salt dust in its dried areas. Therefore, in this research, we tried to investigate the relationship between vegetation and dust in the cities around Lake Urmia. Salinity in plants causes physiological disorders, salt stress causes growth, photosynthesis, protein, respiration, energy production, premature senescence, water reduction in plants. Considering these effects, it was tried to evaluate the overall health of plants by using related indicators including NDVI, CIre, GCI, CRI2, NDWI, NDII, MSI, PSRI. These indicators evaluate the amount of plant water, plant water stress, photosynthesis capacity, plant growth and water deficit, the amount of chlorophyll, nitrogen and pigments, which are related to plant energy and health. According to these indicators, the health of plants is generally in a favorable condition, and mostly the highest numerical values of the indicators were assigned to gardens. Using Landsat and Sentinel 2 images and the NDVI index, the vegetation changes of the region were determined in the period from 2010 to 2020, and then using the MERRA-2 database, the amount of dust concentration was also extracted for the mentioned years. The results showed that the average NDVI in the studied area follows a constant trend with an overall average of 0.2957 and sometimes it increases or decreases due to the influence of external factors such as dust. Based on this, the highest (0.3495) average NDVI is related to 2018 and the lowest (0.2579) is related to 2013. Also, two methods of linear and logarithmic regression were used to investigate the relationship between vegetation cover and dust, and the results showed that based on the linear (0.7703) and logarithmic (0.7915) regression, the highest coefficient of explanation between the two mentioned indicators was in May. have been.

Water resources management, especially irrigation practices, is heavily reliant on reference evapotranspiration (ET0). ET0 is the rate of evaporation and transpiration from a standard reference surface with a presumed surface resistance of 70 s.m-1, the height of 0.12 m and an albedo of 0.23. Penman-Monteith FAO-56 (P-M FAO-56) approach is the most commonly used method for calculating ET0. In spite of the fact that FAO-PM is achievable, its implementation remains inconvenient because it requires a large amount of meteorological data, which is derived from standard meteorological observation stations. In the absence of complete climate data, it is highly desirable to have a model with fewer input climatic dates. Therefore, remote sensing methods have been used and improved over time to estimate ET0 at various spatial scales. Alternatively, it has been observed that the research community has become increasingly interested in obtaining data from metaheuristic algorithms that are based on artificial intelligence (AI).

In this research, it has been attempted to estimate the amount of daily reference evapotranspiration (ET0) using two data-driven models, using a combination of inputs from meteorological station data and satellite imagery data from MODIS sensor, by considering different inputs from these sources. The models include the random forest (RF) and hybridized RF with genetic algorithm optimization (GA-RF). Moreover, the correlation of input variables with ET0 is evaluated and the possibility of training a simple and accurate machine learning model in the conditions of lack or absence of meteorological data using satellite image data is investigated. So, this study aimed to determine ET0 in the time period of 2003-2021 using land surface temperature (LST) data and leaf area index (LAI) acquired from MODIS sensor and Tabriz meteorological station data including maximum and minimum air temperatures (Tmax, Tmin), average temperatures (T), wind speeds (U2), average relative humidity (RH), maximum and minimum relative humidity (RHmax, RHmin), and sunny hours (n). For the study area, daily LST were extracted from the Terra (MOD11A1) and Aqua (MYD11A1) satellites. Moreover, the LST of Terra and Aqua satellites were combined, since the LST values had missing data due to the presence of clouds. Furthermore, MODIS MCD15A3H version 6.1 using four-day data from Terra and Aqua satellites was used to determine the leaf area index (LAI). The standard P-M FAO-56 method for calculating daily reference evapotranspiration was considered as the base method. The set of input parameters was considered based on the cross-correlation of the parameters with reference evapotranspiration obtained from the FAO-Penman-Monteith equation.

The results of two data-driven models including standalone random forest (RF) and hybridized RF model with genetic algorithm (GA) to estimate ET0 values were compared with calculated ET0 by P-M FAO-56 equation. The results indicated that all of the studied input variables are highly correlated with the target variable. Based on the P-M FAO-56 method, the average air temperature with the highest value (R2=0.903) and the wind speed with the lowest value (R2=0.282) has a high and low correlation with reference evapotranspiration. Also, by comparing LAI and LST MODIS parameters, LST has the highest correlation coefficient with ET0 with R2=0.865. A total of twelve scenarios for estimating ET0 are evaluated, each with a different set of input parameters. Based on the correlation between the parameters and ET0, the first ten scenarios are categorized. Additionally, the eleventh scenario is based only on satellite images, and the twelfth scenario is based solely on weather station data. Based on the results, the GA-RF-10 (R2=0.976, RMSE=0.200, MAPE=11.373, and MBE=0.028), which includes all input parameters, outperforms the other models. There was a greater degree of accuracy with the RF-10 (R2=0.949, RMSE=0.293, MAPE=16.442, and MBE=0.017) when compared with the other random forest models. Based on the comparison of scenario 11 (satellite image data) and scenario 12 (meteorological station data), it appears that scenario 12 is more accurate for both RF (R2=0.922, RMSE=0.357, MAPE=20.712, and MBE=0.009) and GA-RF (R2=0.944, RMSE=0.306, MAPE=17.037, and MBE=0.013) models. Despite the fact that only satellite image parameters did not provide accurate estimation of ET0 compared to independent meteorological parameters, the inclusion of these parameters in the ET0 estimation resulted in more acceptable results, demonstrating the importance of satellite image parameters. Thus, satellite data may be useful and recommended for estimating ET0, particularly in areas without meteorological stations.

Drought is one of the greatest challenges of our time due to the dangers it poses to the world. In arid and semi-arid regions, it is necessary to continuously monitor agricultural systems that face water shortages and frequent droughts. Therefore, it is necessary to have large-scale information about agricultural systems and land use for managing and making decisions for the sustainability of food security. Continuous monitoring of drought requires a large amount of information to be processed with great speed and accuracy. Due to the complexity and impact of various factors on drought, in recent years, the methods of combining several factors to create a comprehensive drought index have received much attention. Machine learning and deep learning methods can provide a more accurate and efficient tool to predict droughts and be used in drought risk management. The review of sources shows that until now no studies have been conducted in the field of drought monitoring using deep learning approach and satellite images in the catchment area of Lake Urmia in Iran. A large part of its economic activities is dedicated to agriculture. The increase in temperature, the increase in evaporation-transpiration and the excessive use of water resources for agriculture have caused an upward trend in the frequency of droughts in this basin during consecutive years, one of the harmful effects of which is a significant decrease in the lake level. Therefore, for drought management in this basin, it is very important to identify drought behavior so It is very important to determine appropriate and reliable indicators to measure and predict the effects of droughts. According to the investigations, it was observed that most of the studies in the field of drought in this basin have been carried out from the meteorological point of view, or by individual plant indicators, so in this study, using the approach of principal component analysis, we tried to provide a composite drought index for drought modeling and forecasting.

In this research, satellite images and deep learning and machine learning methods have been used to predict the Combined Drought Index. For this purpose, satellite images were first obtained for the study area and pre-processing was done on the data. Then, all the data were converted to a scale with a spatial resolution of 500 meters, and the VCI index was calculated using NDVI data, the TCI index using the land surface temperature product, and the CWSI index using the Modis evapotranspiration product, and finally, CDI drought index was calculated using principal component analysis method. Then the correlation between CDI data and other meteorological variables including evapotranspiration, potential evapotranspiration, land surface temperature during the day, and land surface temperature at night was calculated. Finally, the CDI index is modeled using deep learning and machine learning methods.

This study modeled the Combined Drought Index based on a different combination of input variables and deep learning and machine learning methods. Examining the results showed that the variables of the normalized difference vegetation index, the land surface temperature during the day and at night, evapotranspiration, and potential evapotranspiration were the most influential parameters for modeling the CDI index, and all four methods with acceptable accuracy and error have been able to model the combined drought index. The CART model with a correlation coefficient of 0.96, RMSE equal to 0.029, and Nash Sutcliffe coefficient of 0.92 was chosen as the best model among the methods.

In this research, different combinations of input variables extracted from satellite image products were evaluated in the form of 6 independent scenarios to predict the Combined Drought Index. By examining the evaluation parameters including correlation coefficient, Nash Sutcliffe coefficient, and root mean square error, it was found that all four methods can estimate the combined drought index with acceptable accuracy and error. Among all the methods, the CART method performed better (R=0.96 and RMSE=0.029) than the other methods for predicting the time series of the Combined Drought Index. On the other hand, the SVM method has been able to model the combined drought index with acceptable accuracy (R=0.94 and RMSE=0.034). However, contrary to expectations, two deep learning methods were able to model the combined drought index with less accuracy than machine learning methods. In general, by examining the results, it was found that with the method presented in this research, it is possible to accurately predict the CDI combined drought index time series and predict drought in different periods of plant growth, and use its results for regional drought management and policies, especially in Basins without statistics.

Accurate estimation of reference evapotranspiration (ET0) is essential in water management in the agricultural sector, especially for arid and semi-arid climates. ET0 plays a vital role in the water and energy cycle and is an essential link between ecological and hydrological processes. Therefore, accurately estimating ET0 is a major issue for understanding the water cycle in continuous soil-plant-atmosphere systems. The traditional ET0 estimation methods are mainly based on physical principles, such as Priestley-Taylor, Hargreaves, and Samani, which have many limitations in accurate ET0 estimation in cases of minimum meteorological parameters (such as radiation solar, wind speed, and air temperature). Numerous studies have focused on ET0 estimation using terrestrial data. However, in the case of a lack of meteorological stations, the conventional methods of estimating ET0 using ground data will be inefficient, so remote sensing (RS) provides the possibility to fill such a gap, in such conditions, satellite images are the most effectivefor evaluating ET0 in large areas. Because satellite images have a suitable spatial and temporal resolution, the time series of satellite images can be used to estimate ET0. The successful estimation of ET0 from satellite images paved the way for its prediction using artificial intelligence models. The primary satellite imagery sources can be obtained from Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Global Land Surface Satellite (GLASS). Remote sensing data provides the possibility of recording more information through satellite images. Remote sensing methods can be used to extract vegetation information and different types of radiation, which help estimate ET0.

In the current research, two different agro-climatic locations including Ahvaz and Tabriz stations were selected. According to De Martonne classification method, Ahvaz was classified as dry climate and Tabriz as semi-arid climate. In this research, random forest (RF) and multi-layer perceptron (MLP) algorithms have been used to estimate monthly ET0 in Ahvaz and Tabriz stations. The input parameters were selected from Landsat 8 and MODIS satellite images in the time period of 2014 to 2021. The utilized parameters were the monthly average, Landsat Land Surface Temperature (LSTLand), MODIS Land Surface Temperature (LSTMOD), Landsat Satellite Normalized Difference Vegetation Index (NDVILand) and MODIS Normalized Difference Vegetation Index (NDVIMOD). To evaluate the accuracy of the input parameters and models, the estimated monthly ET0 was evaluated with the monthly ET0 of the FAO-Penman-Monteth equation.

The input parameters for implemented models were Landsat land surface temperature (LSTLand), MODIS land surface temperature (LSTMOD), Landsat Satellite Normalized Difference Vegetation Index (NDVILand), and MODIS Normalized Difference Vegetation Index (NDVIMOD). Six possible scenarios were defined to estimate monthly ET0. The first two scenarios were considered as a single parameter (scenarios 1 and 2) and other scenarios were evaluated with two input parameters. Scenarios 3 and 4 were evaluated based on the parameters of the Landsat satellite and MODIS sensor, respectively. In scenarios 5 and 6, monthly ET0 was estimated with Landsat and MODIS NDVI and Landsat and MODIS LST, respectively, to determine the effect of NDVI and LST values on ET0 estimation. According to the obtained results, for the MLP and RF models in Ahvaz station, the value of R2 ranges from 0.440 to 0.972 and 0.271 to 0.983, respectively. In Ahvaz station, the lowest and highest RMSE is 0.279 mm.month-1 (RF-5 model) and 1.396 mm.month-1 (RF-4 model), respectively. Additionally, in this station, the highest and lowest values of NS are 0.962 (RF-5 model) and 0.042 (RF-4 model), respectively. According to the obtained results, in estimating the monthly ET0, the best performance is related to MLP-6 (R2=0.972, RMSE=0.348, and NS=0.940) and RF-4 (R2=0.983, RMSE=0.279, and NS=0.962). The highest and lowest values of R2 in Tabriz station were 0.988 and 0.186, respectively. Moreover, MLP-4 and RF-5 models in this station have the lowest and highest RMSE, respectively. The results showed that in Tabriz station, the best performances were related to MLP-4 (R2=0.988, RMSE=0.299, and NS=0.935) and RF-4 (R2=0.979, RMSE=0.302, and NS=0.933). In addition, in this station, the RF-5 model has the weakest performance among all models with R2=0.186, RMSE=1.169, and NS=0.012.

The results showed that 1) the accuracy of monthly ET0 estimation in Ahvaz (arid climate) and Tabriz stations (semi-arid climate) with scenario 4 including LSTMOD and NDVIMOD was better than other investigated scenarios; 2) in estimating monthly ET0 using a single input parameter including LSTLand (scenario 1) and LSTMOD (scenario 2), in both Ahvaz and Tabriz stations, scenario 2 had better performance with both MLP and RF models; 3) estimation of monthly ET0 in Ahvaz and Tabriz stations has performed best with RF-4 and MLP-4 models, respectively, with LSTMOD and NDVIMOD input parameters (scenario 4); 4) in the comparison of scenario 5 (NDVILand, NDVIMOD) and scenario 6 (LSTLand, LSTMOD) in both RF and MLP models, scenario 6 has the best performance in estimating monthly ET0; and 5) in the comparison of monthly ET0 estimation in both arid and semi-arid climates, the best performance with a high correlation coefficient was obtained with the MLP model in semi-arid climates.

Aland Chai basin which is located in Khoy County and North West of Iran is one of the high erodibility susceptibility basins due to its unique topographic location and receiving appropriate rainfall throughout the year, especially in spring. The primary purpose of the present study is to investigate and analyze the role of hydrogeomorphic indicators in the sensitivity to erodibility of the sub-basins of the Aland Chai basin located in Khoy County. For this purpose, 12 hydrogeomorphic parameters including Drainage density, Stream frequency, Bifurcation ratio, Drainage texture, Texture ratio, Infiltration Number, Compactness coefficient, Elongation ratio, Circulatory ratio, Form factor, Relief, and Gradient have been used. Study Area Aland Chai basin with an area of 1147.30 km2 is located in the Northwest of Iran and the Western Azerbaijan province. This basin is located between 38°- 30¢-14² and 38°- 48¢-22² N and between 44°- 15¢- 13² and 45°- 01¢-02² E. Basin elevation variations are from 1093m in the Aland Chai River bed to 3638m above sea level in the Avrin Mountain. This basin is one of the sub-basins of the Aras River basin, which surface water flows into the Aras River after joining the grand Qotour River.

In the current study, a geomorphometric ranking method was used considering two groups of hydrogeomorphic parameters to rank the parameters. The first groups are the parameters that have a direct relationship with the erodibility of the sub-basins. These parameters include Drainage density, Stream frequency, Bifurcation ratio, Drainage texture, Infiltration number, Relief, and Gradient. The second group includes 4 parameters of Compactness coefficient, Elongation ratio, Circulatory ratio, and Form factor, which have an inverse relationship with erodibility sensitivity. Next, in order to standardize the geomorphometric rating of each of the parameters, equation 1 is used for the first group and equation 2 for the second group. Equation 1) .Equation 2)

Hydrogeomorphic analysis of basins plays an important role in analyzing the hydrological behavior of basin. In this study, 12 hydrogeomorphic parameters were analyzed from three aspects of drainage network characteristics, shape parameters, and relief characteristics of the basin in order to investigate the role of these parameters in the sensitivity of the Aland Chai basin to erosion. In the next step, the information of each sub-basin based on 22 hydrogeomorphic parameters were prepared using the geomorphological laws of Horton, Schumm, and Strahler in ArcGIS software. In the following, the final map prioritizing the sub-basins in terms of sensitivity to erosion in 5 classes of very high (<43), high (39-42), medium (33-38), low (30-33) and very low (>30) sensitivity was prepared.

The results of erosion susceptibility of sub-basins using hydrogeomorphic parameters also showed that 5 sub-basins (sub-basins 1, 2, 3, 4, and 11) have high susceptibility. Land use changes were investigated in the Aland Chai basin between 1985 and 2020. To prepare a land use map, Landsat time-series images and supervised classification method with Maximum Likelihood Algorithm in ENVI software were used. The results of this part of the study showed that the area of ​​the Rangeland had a decreasing trend between 1985 and 2020. In contrast, with the physical development of Khoy and Firouraq cities during the statistical period under study, we are witnessing an increasing trend in the area of ​​built-up areas. Agricultural and garden classes have also increased in 2000, 2010, and 2020 compared to 1985, but the largest increase in agricultural land and garden area is related to 2000. From 2000 with the physical development of Khoy city (development of settlements and facilities) and the expansion of surrounding villages and the integration of some villages with Khoy city (such as Badal Abad village) and the conversion of agricultural lands into urban areas, we see a downward trend in agricultural lands and gardens. The study of the relationship between land use changes in each sub-basin and their erodibility showed that in the sub-basins upstream of Aland Chai basin, land use change (an increase of agricultural lands) or inappropriate and excessive use of area capacity, it is effective in intensifying erodibility of sub-basins.

Estimation of Actual Evapotranspiration of Pasture Plants using SEBAL Algorithm (Case Study: Ahar County)Abstract The present study was an attempt to calculate the actual evapotranspiration rate of thyme, mint, and alfalfa pastures using the SEBAL method in the Ahar region, which is located south of Arsbaran forests and has numerous pastures of different species. For this purpose, 6 Landsat 8 satellite images were used between 2017 and 2020, which were in the initial and final periods of growth. The results were compared with the Penman-Monteith method. The results indicated that, based on the SEBAL method, the mint crop had the lowest rate of evapotranspiration in the initial period of crop growth on 2021/5/29, with a numerical value of 2.84 mm per day, and the alfalfa crop also in the initial crop growth period on 2019/6/11 has the highest rate of evapotranspiration with a numerical value of 3.49 mm per day. Additionally, in the final period of growth in the SEBAL method, the mint crop on 2018/8/28 had the lowest rate of evapotranspiration with a numerical value of 6.18 mm per day. Also, the thyme crop had the highest evapotranspiration rate in the final product growth period on 2022/7/19, with a numerical value of 7.41 mm per day. Finally, based on the comparisons made between the studied methods in the initial and final periods of growth in terms of squared mean error, mean absolute deviation, and coefficient of determination, it can be concluded that the SEBAL method compared to the Penman-Monteith method has an error rate with RMSE of 0.717, MAD of 0.658, and the coefficient of determination of 0.84 mm per day, which is acceptable.Keywords: Evapotranspiration, SEBAL, Pasture, Water requirement, Ahar County

Estimating reference evapotranspiration (ET0) is a fundamental requirement of agricultural water management. However, the lack of necessary meteorological data makes it difficult to estimate ET0 using the FAO-Penman-Monteith equation wider areas. Therefore, this research examines the estimation of daily reference evapotranspiration using MODIS Land Surface Temperature (LST) from satellite imagery in two climates of Tabriz and Rasht. ET0 has been estimated using two random forests (RF) and random forests optimized with genetic (GA-RF) algorithms. The parameters used in both stations include the combination of daily land surface temperature (LSTday), nightly land surface temperature (LSTnight) and average land surface temperature at night, and day (LSTmean). The obtained results indicated that LSTmean has an excellent ability to estimate ET0 in both stations. In Tabriz station with a semi-arid climate, GA-RF-7 model with RMSE=0.516 and in Rasht station with a very humid climate, the GA-RF-5 model with RMSE=0.868, have the best performance among the studied models. Moreover, the evaluations revealed that the temperature of the earth's surface at night is as important as the temperature of the earth's surface during the day, and by combining these two parameters, satisfactory results may be obtained.

Attention to Ardabil province is one of the important centers of wheat cultivation, determining the level of cultivation will be of particular importance in economic and political planning and can ensure the country's food security, but in the past, most traditional methods, such as field measurement, etc., have been used to determine the level of crop cultivation, which has many errors, In this study, we tried to use Landsat 8 satellite imagery, Vegetation Indices (NDVI, SAVI, DVI, GVI, IPVI, RVI), Principal components analysis (PCI), Optimum Index Factor (OIF), and classification algorithm to estimate the maximum probability of wheat cultivation area in the study area. Based on the results of main components analysis, more than 99% of data variances were explained in three main components and the best color combination of the OIF index was determined by bands 5, 6, and 7 with a numerical value of 8383.73. Also, the results showed that wheat cultivars under cultivar 94-95 in the studied area with kappa coefficient is 0.87 and the general accuracy of 95.2% were 59203.07 hectares and According to the statistics of agricultural Jahad official in Ardebil province, which is about 62480.21 hectares, In other words, the difference is 5.24% or about 3277.14 hectares which seem to be acceptable. Therefore, it can generally be concluded that Landsat 8 images, Principal components analysis, and Optimum Index Factor are highly effective in determining the level of wheat cultivation.

In this paper, the Poisson Point Process model has been used to analyze the frequency and intensity of hot and cold extreme temperatures at Abali station. The Poisson Point Process Model (Paritou- Poisson) is designed based on Peaks over threshold method. In this method, a suitable temperature threshold was first selected using statistical methods and then the Paritou-Poisson distribution was fitted to values higher than the threshold.  Estimation of distribution parameters was carried out using Maximum likelihood Method and extreme temperatures were estimated for different return periods. To validate the model, Q-Q charts and χ2 statistics were used. The results showed that the Poisson point process model has a great potential for modeling the extreme temperatures

 Monitoring and investigation of land use changes in forest areas provides acceptable information for efficient management of these resources. Also, taking care protecting natural resources requires awareness of the conditions and how to change different land uses. Therefore, the purpose of this research is to evaluate the change of forest use in the forest area of ​​ Fandoqlu from 2010 to 2019 by using Landsat 5, 8 images and integrating them with Sentinel 2, Ester images. After preparing images from the years 2010, 2015 and 2019, geometrical, radiometric and atmospheric corrections of the images were done and the classification accuracy using kappa accuracy was% 93, %83, 91% respectively. The land use of Fandoqlu forest area was To model the change of use for 2025 with the Geomod model, it is necessary to prepare a suitability map of the area, which is prepared using the Fuzzy ANP method and the incompatibility coefficient is less than 0.06. In order to prepare a suitability map of four general factors: human, biological, topographic and climatic, and 12 sub-criteria were obtained with Boolean functions, and Boolean land use maps (forest and non-forest) 2010 and 2015 were modeled for 2019 and for modeling Land use for 2025 was done from the base map of 2019 and the transition matrix of Markov chain of land use in 2025 with the CA-Markov model And the result of location changes for 2025 was obtained. To evaluate the accuracy of the model, the agreement and non-agreement of pixels with Klocation and Standard were done with 98 and 95 accuracy, respectively. Modeling results for the year 2025 changes in a decreasing manner; The increase of non-utilized covers and the reduction of forest use, which will decrease from 3204.18 hectares in 2010 to 3070.55 hectares in 2019; According to the results of the human criterion and the sub-criteria of land use and distance from the road, the tourism potential of this area and the attraction of tourists as well as the interference of local residents can have a direct effect on this forest reduction process.

 organizations, people and local, is the only way to protect the forests of this region. In this study, remote sensing data such as satellite images of Landsat8,5, ASTER and Sentinel 2A were used to prepare the baseline map. Climatic data of all parameters up to 1396 were received from the synoptic station of Ardabil province. The digital model of 12.5 altitude was prepared from NASA website to prepare slope maps, slope direction, border layer of the study area and vegetation layer from Ardabil Natural Resources Organization. The research used Arc GIS, ENVI 5.3, TerrSet, eCognition 9 Google Earth pro and SUPER DECITION software. then based on the value and purpose of Reclassify and layer fuzzy. to predict the future conditions of forest cover changes by GEOMOD method, a time map of the start of the modeling process and a map of change appropriateness are needed. Geomod is used to model spatial patterns, forecast and probability of change. GEOMOD is used to simulate patterns of spatial change of use or change between two categories of use (forest and non-forest).

 In order to implement the GEOMOD model, a fit map prepared from the study area is required. Fuzzy ANP method was used to prepare the appropriateness map of the study area, which has four criteria: human (distance from the road, distance from the village, population), topography (slope, direction, height) and biological (land use, lithology, soil), criteria. And the following criteria are used in the map. Climatic parameter (average annual rainfall, temperature, altitude, slope, direction of slope, waterway) was used. 2025 user is required, so using 2015 and 2019 user with CA Markov model for 2025 was modeled. Decreased accuracy was associated. The results of predicting forest spatial changes for 2025 were used from the 2019 Boolean user map and the CA Markov modeled user map. Conclusion To implement the GEOMOD model, we need a fit map for spatial modeling of changes. In this study, four criteria and 12 sub-criteria discussed in Chapters 3 and 4 were used to prepare a fit map of the region. They have acquired Super Decision software.

Using the Boolean forest and non-forest boards and the 2015 and 2019 land use maps with the CA Markov model for 2025, it was modeled. Human, climate and biological have weights of 0.358, 0.258, 0.203 and 0.165, respectively, which the topography achieved the highest weight in Super Decision software. Among the sub-criteria, the type of land use has a high impact on changes in the region. The final output of the fit map was prepared by applying the OR function after applying the weights, which had a better result than the other functions. Finally, using the 2015 and 2025 user maps for 2025, forest spatial changes were made. To evaluate the accuracy of the model, the agreement and non-agreement of spatial pixels were used, which was modeled with Kappa 98% for 2019. The results of spatial change modeling show the high accuracy of the model in predicting spatial changes. GEOMOD results for 2025 will reach 3085 thousand hectares from 3151.9 hectares. Research conducted in different places. the country indicates a decline in forest areas in the coming decades.

Adherence to non-operating defense principles in locating infrastructure, especially military centers, can reduce vulnerability and damage in times of danger and crisis and any military threat. There are several factors affecting this location, most of which are natural geographic (climatic and geomorphological) factors. Considering the development of cities and the placement of military centers within the urban context and the lack of adherence to non-operational defense principles in their placement and construction in the past, as well as the need to relocate most of the existing military centers at the provincial level, the importance of the case doubles. One of the main goals of the research is. Two groups of data were used in this study. Climatic data obtained from the Provincial Meteorological Organization and other data obtained from topographic and geological maps and related organizations The present study deals with the role of climatic elements and other effective factors in locating military sites. The purpose is to identify areas that are prone to non-operating defense principles. The strategic position of the East Azerbaijan Province in northwestern Iran in order to align with the Republic of Azerbaijan and Armenia and the Nakhchivan Autonomous Republic and to position itself on the European and Central Asian communication axis further emphasizes the importance of non-operational defense principles in locating sensitive and military centers. . The research method in this research is based on the Network Analysis Process (ANP) and GIS analysis. After weighting the layers and overlapping them and examining the principles of non-operating defense, the final mapping was prepared and the results show that among the factors Effective in locating military centers in the study area, climatic factors have the highest weight (0.34).

In the beginning of spring, floods are the most important geomorphic hazards in Iran, destructing propertiesas and human lives. Aland Chai basin, located in Khoy County (northwest Iran), is also known as one of the basins with high potential for flood hazard due to its special geographical situation. This study tried to model spatial variation in flood hazard susceptibility in this basin using the ensemble model, FURIA-GA-LogitBoost. For this purpose, 13 effective parameters of flooding including lithology, soil hydrological groups, NDVI, land use, slope, aspect, elevation, distance to the river, river density, precipitation, topographic wetness index, stream power index, and sediment transport index were used. WEKA software was used to implement the research model and the final flood hazard susceptibility map was prepared. The study found that downstream areas of the basin have a high flood hazard susceptibility. These areas contain the most important human settlements (Khoy city) and agricultural lands and flood as a geomorphic hazard can seriously damage them. Considering the ROC curve and area under the curve (AUC), it was found that the FURIA-GA-LogitBoost model performed well in the preparation of flood hazard susceptibility map with coefficients of 0.861 and 0.895, respectively, in training and validation data.

The increase in motor vehicles and the increasing use of cars in the last half century have had adverse effects. These include the number of accidents and the amount of financial and fatal accidents per year. Various factors may affect road accidents, some of which are inevitable and some can be controlled. Among these factors, we can mention the problems of geometric design of the course, environmental conditions and human factors. Accordingly, in this research, identifying identifying road accidental points in the transit roads of East Azerbaijan province using ANP and BWM models. The BWM model as a new model is one of the multi-criteria decision-making models that Taking into account the best and worst criteria, weighing criteria and sub criteria and It can be used in various spatial modeling. In this regard, four main criteria (Climate factors, Environmental factors, Topographic factors, Road factors) and fourteen sub-criteria were used. In order to weight and evaluate the importance of criteria and sub-criteria, two models of BWM and ANP have been used and After calculating the weight of criteria and sub-criteria and integrating them with the standard layers, the final map of the potential road accident occurrences is based on both models. Based on the results, the criterion of climatic factors in both models is of the highest importance in road accidents and the criterion of environmental factors is least important in this regard. Below are the criteria used in both models, Under the rainfall criterion, the most important and the lowest criterion of the river have the least importance in the occurrence of road accidents. The most dangerous route in terms of location in both the Keshkeri-Marand route and Jolfa-Nordoz route is the least dangerous route among the transit routes of the East-Azerbaijan province. The results of the comparison of the two models show that the BWM model with a Consistency Ratio of 0.075 was better than the ANP model with a Consistency Ratio of 0.09. Also, the matching of the results from the two models used with ground control points indicates the greater accuracy and efficiency of the BWM model of the ANP model

The valuable Arasbaran forest is a complex and dynamic ecosystem that has always been subject to extensive fires. The purpose of the present research is to utilize the technology of remote sensing and geographic information system and the technical abilities of the Google Earth Engine system in order to prepare a fire occurrence map in the rangelands and forests of Arasbaran. In order to choose the appropriate method and type of satellite between Sentinel2 and Landsat8, the separability index was used. Accordingly, the RdNBR differential index was selected among the different indicators of fire detection to prepare the final map of the last 9 years and the cumulative fire map. Based on the accuracy assessment of the resulting map quantitatively, 84% of the actual fire points recorded by the General Directorate of Natural Resources and Watershed of East Azarbayjan province were placed at a distance of 200 meters from fire polygons extracted from satellite images, which showed the high accuracy of the fire map. The field visit also showed a good match among the fire areas resulting from the processing of satellite images with the existing situation of the region. The current research showed the high potential of these two satellites as well as the extraordinary ability and facilities of the Google Earth Engine system in providing a huge amount of remotely sensed data and advanced processing on them to prepare fire occurrence maps. The advantages of Landsat8 compared to Sentinel2 are having thermal bands and more time series. The spatial and radiometric resolution of both are almost similar, and the low-temporal resolution of Landsat8 will be compensated by combining it with Landsat 9 images. Therefore, in line with the results of similar studies, Landsat8 is generally preferable to Sentinel2. For the correct and scaled spatial data from Arasbaran region, it is suggested to create an integrated and large-scale Geo-database due to the lack of accuracy.

The water cycle in nature is directly related to the climate of that region. Reasonable and correct use of water resources requires accurate quantitative and qualitative knowledge and collection of appropriate climate data and information. Depletion of groundwater reservoirs, drying of canals and springs and even semi-deep wells and reduction of deep well discharge, change of groundwater flow direction, salinization of aquifers, salinization of soil due to irrigation with saline water, barren The emergence of fields, soil erosion, etc. has put most of the plains of the country at risk of further desertification (Tavousi, 2009: 14).Atmospheric precipitation is the main source of surface and groundwater and the study area is poor in terms of atmospheric precipitation and its amount is between 150 to 450 mm per year, which varies in plain and mountainous areas. The climate of the region is semi-arid and cold and is mostly influenced by the Mediterranean climate. Due to the fact that groundwater is the most important source of water consumption in the study area, the impact of climate change, especially precipitation on the water table of wells in the area was investigated in this study.

To study the trend of groundwater level changes in Marand plain, water table data of 23 piezometric wells and data of 8 rain gauge stations during the last 16 years of 1395-1395 were used. After using the correlation matrix method to select rainfall stations and considering the complete statistical data and appropriate coverage of the area by these stations, 4 stations were selected for the study and for each station, a piezometric well was selected within the station. This research was first calculated using precipitation data and water table of piezometric wells SPI and SWI values ​​and then NRMC values ​​for each index, respectively, in each method are briefly referred to:Calculate SPI and plot seasonal SPI variations of selected stations.The standardized rainfall index was provided by McKay et al. (1993, 1995) to provide a warning and help assess drought severity and is calculated by the following formula:  Relation 1:SPI = (X_ij-X_im) / σ In the above relation, X_ij is the seasonal rainfall at rainfall station i, with j number of observations, X_im is the long-term average rainfall and σ is the standard deviation Calculate SWI and plot the seasonal SWI of selected wells  The standard water level index was presented in 2004 by Bui Yan et al. (2006) to monitor fluctuations in groundwater aquifers in the study of hydrological droughts, which is calculated by the following formula:Relation 2:   SWI = (W_ij-W_im) / σ Where W_ij is the seasonal average of the water table of observation wells i to j, W_im is the long-term seasonal average and σ is the standard deviation. Calculate the NRMC values ​​of each indicator and plot the normalized distribution curve In this method, seasonal normalized distribution curves were adjusted for both SPI and SWI indices. Cumulative normalized curve is a kind of condensation diagram of a climatic or hydrological variable (such as precipitation and water table) that is extracted from the subtraction of each observation in the statistical series of the long-term average and its division by the average according to the following formula. (Rasooli, 1994) Relation 3:  NRMC xi = ( (Xi-X m) / ({(Xi-X ̅m) / X ̅m})  ) * 100  In the above formula, Xi represents the amount of each rainfall observation or the amount of water table and X ̅m is the long-term average in the series of observations.

Investigation of normalized distribution curves showed a correlation between precipitation changes and groundwater level in Marand plain. This correlation has a higher significance with a delay season. Shamsipoor (2003) in Hamedan plain achieved a 9-month delay between precipitation and water table. Mohammadi et al. (2012) in Arak plain expressed the impact of groundwater resources from drought with a delay of two months. The results of the study (Rudel and Lee 2014) in the study of groundwater drought index in the United States showed that the SPI drought index with a delay of 12 and 24 months had the highest correlation with the SWI index.

Considering the more fluctuations of the water table than the fluctuations of the rainfall, it can be concluded that human factors such as uncontrolled harvesting is an effective factor on the water level of wells. Komasi et al. (2016) stated the effect of human factors on the decrease of groundwater level before the factor of climate change in Silakhor plain. Calculations showed that the value of correlation for both SPI and SWI indices in the nonlinear multivariate equation is higher than the value of the linear equation, which indicates the effect of several other factors in addition to precipitation fluctuations on the groundwater level. According to the results of the study, it seems that the groundwater level in addition to precipitation depends on other factors such as geology, lithology, tectonic morphology, the shape of the aquifer, the distance of aquifers to the feeding site and .... And to achieve more complete results, it seems necessary to address these factors in future research.

Human society and the natural environment have been affected by the phenomenon of urbanization and urban development. The physical growth of cities can have many environmental, economic and social consequences. Changing land use and land cover in cities causes many problems. The purpose of this research is to identify the urban growth and its effect on the thermal pattern using RS. The data used in this research are the images of satellites and Landsat 5 & 8, TM and OLI, TIRS sensors from 1990 to 2019, which were classified using the object-oriented classification technique and then to predict the expansion of Tabriz city for 2029. Markov model and automatic cells (CA-Markov) have been used, Split Window and Mono Window methods have also been used to extract the surface temperature. The results of the research showed that the air temperature of the residential areas has increased by about 1 degree and the highest temperature calculated for the residential areas is in the southwestern parts, which is more than 29 degrees Celsius, as well as the vegetation cover in 1990, 2000, 2010 and 2019 was 81, 44, 61 and 43 square kilometers, respectively, and urban lands were 54, 81, 100 and 128 square kilometers, respectively. which clearly shows the reduction of vegetation and the increase of residential areas, the analysis of the results of the expansion pattern of Tabriz city also showed that the most expansion was in the southern, southwestern and eastern parts.Human society and the natural environment have been affected by the phenomenon of urbanization and urban development. The physical growth of cities can have many environmental, economic and social consequences. Changing land use and land cover in cities causes many problems. The purpose of this research is to identify the urban growth and its effect on the thermal pattern using RS. The data used in this research are the images of satellites and Landsat 5 & 8, TM and OLI, TIRS sensors from 1990 to 2019, which were classified using the object-oriented classification technique and then to predict the expansion of Tabriz city for 2029. Markov model and automatic cells (CA-Markov) have been used, Split Window and Mono Window methods have also been used to extract the surface temperature. The results of the research showed that the air temperature of the residential areas has increased by about 1 degree and the highest temperature calculated for the residential areas is in the southwestern parts, which is more than 29 degrees Celsius, as well as the vegetation cover in 1990, 2000, 2010 and 2019 was 81, 44, 61 and 43 square kilometers, respectively, and urban lands were 54, 81, 100 and 128 square kilometers, respectively. which clearly shows the reduction of vegetation and the increase of residential areas, the analysis of the results of the expansion pattern of Tabriz city also showed that the most expansion was in the southern, southwestern and eastern parts.

Various physical, semantic and functional factors in the role of the city as a bed to meet the needs and a container for the activities and behaviors of residents are effective, among which the structure and visual values ​​of the environment play an important role in improving the quality of urban space (Hosseini and Et al., 1390: 84). Among these, one of the urban elements that can play an important role in promoting the quality and visual values ​​of the environment and the appearance of the city is the sky line.  The skyline is one of the most influential factors in arranging urban spaces and planning appropriate urban environments, etc.  A factor that is rarely seen in metropolitan areas today.  It seems that in order to have a diverse and at the same time harmonious urban skyline, in the first place, it is necessary to reconnect the fields of philosophy, art and daily life, which are free from the abstract and abstract view of experts.  The development of interdisciplinary professions such as GIS, architecture and urban design is one of the measures in this direction that can have a different and more accurate view of the urban skyline.  Therefore, the need for advanced geographical models for spatial three-dimensionalization and decision-making and forecasting goals in urban planning and design, especially in the case of vertical evaluation and development, is very clear and necessary.  The advantages of using three-dimensional GIS models can be high potential in urban evaluation and analysis in various dimensions of urban aesthetics and high technology in the field of spatial analysis, the ability to test urban proposals and the power of semantic and topological storage.  Mentioned above.  Therefore, the use of these softwares to analyze urban issues is very important and increases the accuracy of urban planners and managers.  For this purpose, the present study seeks to investigate the status of the urban skyline in the study areas and based on this, the importance of the perceptual and managerial dimension of the issue is analyzed using new methods and techniques.

 The analytical approach of the research is physical and the method used in this research is three-dimensional vision analysis.  Also, Arc GIS software has been used for easy preparation of the environment in 3D to perform the required analyzes.  The data set used includes: digital model data of elevation, surface and floor layers of urban blocks.

   In the present study, the concept of skyline was analyzed using three-dimensional GIS analysis in areas of urban areas 1 and 5 located in the east and northeast of Tabriz.  Areas of interest include Valiasr town and Rushdieh town.  So far, many different methods have been proposed to display and analyze the skyline.  Some of these methods were reviewed in the research background and their strengths and weaknesses were identified.  But the purpose of this article was to investigate the capabilities of 3D analysis and modeling through the development of 3D GIS in skyline display and analysis.  Evaluating the results obtained in this research and comparing it with the results of studies in this field shows that three-dimensional analysis of GIS and 3D GIS development in comparison with other methods, provided there is accurate and comprehensive information, has high efficiency and capability.  The research hypothesis is confirmed.

The skyline is important both in terms of urban architecture and in terms of urban planners and designers; Although these specialties are different in terms of information extraction methods, methods and analytical contexts, but they are quite similar in terms of results.  In general, it can be said that the skyline is one of the analyzes that are analyzed and evaluated in different software environments such as Arc GIS and in different fields.But the results of this study proved in another way that the three-dimensional GIS environment provides an opportunity to easily view and analyze the level of impact, effectiveness, volume, dimensions and angles of the skyline; This software also has high analytical power.  While the analysis of the sky line in a two-dimensional environment gives a graphical and dimensionless result, percentage and frequency.  But the most important difference and the ability to use 3D GIS analysis is that it provides a platform where even people with little knowledge of urban views can easily see the results and make decisions based on it.  Also, the capabilities and capabilities of GIS in the field of various valuations and combined analyzes of vector and raster functions, as well as the presentation of three-dimensional analysis, have led its applications to urban design.

Investigating the effect of drought on water resources of countries plain is high important at optimal management of water resources in the agriculture and natural resources part. The phenomenon of climate change, affects the amount of water existence in aquifer by changing amount of precipitation. The occurrence of consecutive climate droughts affects ground water resources. Knowing and awareness of the effect of time between two phenomenon of drought and hydrological drought, can help managers and planners of the water sector. Over the years, the effect of drought on ground water resources less attention has been paid. In order to understand the state of groundwater resources and optimum management, it is necessary to carry out a thorough study of groundwater fluctuations. In this research, Marand plain is the purpose of this study. Marand Plain is poor in rainfall and has a rainfall of 450 mm / year and at least 150 mm / year which varies in the plains and mountainous regions. In this research, we have tried to investigate the effect of atmospheric drops, including rainfall, on ground water level in the Marand watershed.

The Marand plain with 45 °, 15 to 50 minutes east longitude and 37 ° 7 'to 38 ° 56' north latitude and with an area of 42.517 square kilometer is one of the vast plains in the northwest of East Azarbaijan province. Which is selected as the study area. In this study, in order to study the trend of ground water level changes in the Marand Plain, the static surface data of 23 piezometric wells was used during the 2000 to 2016. First, a common statistical period was chosen to analyze the data series (2000 to 2016). Then in order to reconstruct the statistical defects, the correlation between stations and piezometric wells and linear regression method was used. The IDW method was used to calculate the average rainfall of the plain. Finally, the standard water level index (SWI) and the SPI index for the studied basin were calculated and analyzed. 

The aim of this study was to investigate the effects of climate drought on the fell of groundwater level in the Marand plain using SPI and SWI indices. Meteorological drought conditions in the Marand plain were calculated using the SPI index on a 12-month time scale. The results and drought accuracy of the rain gauge stations in the studied basin showed that during the study period, the first period of drought since 2005 started gradually with decreasing atmospheric precipitation and continued until 2007 and after a period of humidity short-term, again, a short period of drought from 2008 to 2009 has been on the ruling area. The SWI index was used to survey the status of groundwater level. This indicator also showed that in terms of time and place, the drought based on this index corresponded to the drought caused by the SPI index.

Using the SPI index, the drought trend was studied in the region. The results showed that during the study period (2000-2016) three drought periods from winter 2005 to beginning of 2009, summer of 2011 to the end of 2012 and winter of 2015 to summer of 2016 occurred. Drought affected areas included the east and center of the study area and the west of the region witnessed more atmospheric precipitation. The SWI index was used to survey the status of groundwater level. The index showed that in terms of time and place, the drought based on this index corresponded to the drought caused by the SPI index. Data analysis showed that these two indices with a time interval of one season had a correlation of 1%. This means that the hydrological drought after a season has a direct impact on the surface of the water. In general, we can conclude from the results of this study that the trend of ground water surface changes has been consistent with the drought and weathering changes in the region. Therefore, the fall of the ground water level of Marand plain can be largely influenced by weathered droughts.

Public art has followed a new trend in urban spaces in recent years, so that cities invest highly in the design and implementing of public works of art and improving the visual quality of urban spaces, since it is effective in creating good urban spaces and making the city attractive and memorable. It also encourages people to communicate with works of art and each other. Although public art as an art integrated with the public space of cities is not a new concept, many changes have been created in public art and it caused the creation of various types of public art in most cases from a purely static art to a dynamic and interactive art. This article aims to investigate the effect of public art in urban spaces on promoting the perceived vitality of young people. Despite the importance and role of public art in the quality of urban public spaces, studies conducted in the area of public art have not paid much attention to the effect of different types of public arts in public spaces on vitality of people, especially young people. Thus, this study was conducted with the aim of answering the question of “which type of public art has a greater impact on promoting the perceived vitality of young people in urban spaces?”

The present study is a qualitative-quantitative (mixed) study conducted as a survey. In the present study, visual approach and image reading were used to collect information.  Visual research is a term that includes methods based on data collection and analysis of them through designing, painting, photography, film, or video images.  In this article, public arts and interactive arts were divided into different types based on theoretical foundations and research literature and  an image was selected from each type of art and through an online questionnaire was submitted to the participants and they were asked to express their level of vitality from watching that work of art in urban spaces on a 5-point Likert scales. This questionnaire was designed and sent to the target population through cyberspace applications.  Since the target population of the present article was young people, the sample was selected among the students of Tabriz University and the students of Nabi Akram Institute of Higher Education in Tabriz in the second semester of 2020-2021. To determine the sample size, Cochran's formula with an error level of 5% was used. According to the number of students in these two universities (27000 people), the sample size of 379 was selected. After sending the questionnaire to nearly 380 people, 144 people completed the questionnaire. In this research, both art and architecture students and non-art and architecture students in both bachelor and master levels were surveyed.

The research results revealed that among different types of public arts in urban spaces, sound and digital arts such as musical fountain, and the integrated art embedded in the elements available in space such as wall relief in the body of the building and the art appropriate to the site, such as plants formed in parks, regardless of the three criteria of gender, age, education and field of study, are accepted by all young people and increase vitality, but abstract static art is less welcomed by young people. Among the interactive arts, dynamic-passive interactive art and static interactive art obtained the highest score in terms of creating a sense of vitality among young people, and in contrast, dynamic-dynamic interactive art obtained the lowest score. Art related to public and local culture caused the highest vitality among male young people and young non-art and architecture master students, and digital art caused the highest vitality among females and bachelor art and architecture students. In addition, among non-art and architecture bachelor students, two-dimensional applied art obtained a relatively high score.

 The results show that creating static works of art is not effective in designing cities and using public art to create a sense of vitality in urban space and young people show higher tendency to dynamic work of arts. Also, interactive works of art are more acceptable than non-interactive types, and urban planners should pay attention to this issue in embedding works of art in urban spaces and take right decisions and actions to increase presence, interaction, and vitality among young people in urban spaces. Also, the use of modern technologies and creativity in the artistic design of cities is one of the requirements for adapting the urban space to spirits of young people.

We amid to identify and introduce nature-based tourism and its potentiality in West Azerbaijan province. The study area is famous for its environmental and natural landscapes which accordingly implies significant potentials for developing sustainable ecotourism which accordingly leads to improve the economic situation of this province as well. Within this research we applied GIS spatial decision support systems (SDSS) for data analysis and modelling the sustainable ecotourism in west Azerbaijan province. For this goal, 28 relevant criteria from different resources (e.g. physical properties, infrastructure, and facilities) were identified and processed using GIS-SDSS. GIS data aggregation method was applied to develop a final susceptibility map. Results indicated that about 57% of the study area represent a sustainability degree of moderate to significant for ecotourism development. The rest of 43 % basically locates in urban area, deserts resulted by Urmia lake drought which are not indicating a significance potential for ecotourism development. It has also to be highlighted in context of the 57 % there are also different level of sustainability in each area but results shows that ecotourism can be considered as one the main economic development in the study area. We conclude that, the obtained results are very important and critical for identifying nature-based tourism potential assessment. The results of this study provided valuable information for improving the situation as well organizing the sustainable ecotourism in the study area. Results are also great of important for decision makers and planners in light of developing a sustainable natural tourism.