مصطفی کابلی زاده

 Oil and gas pipeline projects face a wide range of safety and security risk factors globally, especially in oil and gas-producing countries that have unsafe environments and poor safety records. Inadequate information about the causes of pipeline failures and poor knowledge about safety and security hinder efforts to reduce such risks. Today, pipelines are widely used to transport oil and gas over long distances; Therefore, their risk assessment can help to identify related risks and take necessary measures to eliminate or reduce the resulting consequences. By reviewing the previous studies, it was found that the goal of the researchers was often to investigate the possibility and consequence of quantitative risk assessment in pipelines, while these parameters are based on the physical characteristics of pipelines and alone cannot cover all aspects of risk along the path of oil and gas pipelines. to cover At the same time, paying attention to the general Feild of the pipeline in an oil region and the uses that include these feild are very important from the environmental, sustainable development, and crisis management aspects. Finally, it seems that not paying attention to this aspect of the goal is one of the distinguishing features of this study compared to other studies. The main goal of this research is to implement a new approach to assess the riskiness of the overall Feild of pipelines on environmental resources around the Maroon oil region.

 In this research, first, the study model of pipeline risk (from the point of view of consequences after the event) was selected. In the next step, the factors affecting the riskiness of the pipeline (in case of various incidents such as fire, explosion, toxic gas leakage, etc.) were identified and quantified. Then, the initial risk zoning map of pipelines in the study area was modeled based on MATLAB software's Mamdani fuzzy inference system. In the final step, to validate the proposed model, the results and outputs of the pipeline risk zoning model based on the fuzzy inference system were compared with the general opinion of experts. Finally, the final risk zoning map of the region was prepared and combined with the route of existing pipelines in the area. became

 Based on the risk zoning of the oil and gas pipeline route from the point of view of facilities/structures, this route mainly passes through areas with low and medium risk that have an area of 223.32 Km2, this area is equivalent to 18.4% of the total area of the oil field. However, about 1.45% of the oil field area includes areas with high risk. These areas are located in the southern parts of the Maroon oil field. According to the land use map, the use of these areas includes main roads, bridges, and power lines. The reason for the high risk in these areas is that in case of accidents related to oil and gas transmission lines, the consequences of these risks in these areas can be significant. The results of risk zoning of the pipeline route from the perspective of environmental factors show that the route of pipelines passes through areas with low, medium, and high risk. But with the difference that, compared to the zoning map of facilities and structures, the passage route is more in areas with medium to high risk. The area of these areas is equal to 750.25 Km2 and includes 61.83% of the total area of the oil area, the use of these areas is consistent with the network of waterways and flowing sand (sand hills). The final risk map shows the path of oil and gas transmission lines, which is the result of combining two environmental and technical criteria. In this map, after zoning the area in terms of low, high, medium, and high risks, the route of the pipelines was adapted to it. In the final map, the route of the current pipelines in the Maroon oil region shows a great adaptation to the areas with low and medium risk. Most of the pipelines' route corresponds to areas with low and medium risk. Finally, the adaptation of the current pipeline route in the region and risk zoning in the northern and central parts of the field showed that these areas show a low and medium level of risk. According to the results, the area of high-risk areas in the oil field is 17.55 Km2, equal to 1.4% of the total area of the Maroon oil field. The total length of oil field pipelines is 249.14 km, the total of 7 km of this route, which is equivalent to 2.8% of the total length of the lines, passes through high-risk areas. Most of this route is located in the south and southeast part of the oil field.

 The results of this research show that the method of expert systems, artificial intelligence, and remote sensing can to a large extent overcome the shortcomings of the lack of zoning maps of general risk from the route of oil and gas pipelines in a vast oil area. The relationship between input and output information in the proposed fuzzy inference system was described as linguistic variables by applying expert opinion, which is more flexible and accurate compared to the classical model. Finally, based on the results of this study, the southern parts of the Maroon oil field are of high risk due to the existing pipeline route. This route is approximately 7 km long, i.e. 2.8% of the entire pipeline route. Finally, considering the above, it can be said that the above-mentioned method is effective in better decision-making by experts, because achieving a general view of the level of risk in different areas, in adopting strategies for prevention, evaluation, and correction of the area, is useful. has a lack of need for field studies, as well as reducing the burden of costs related to it, as well as saving time, are among the most important parameters that will make the research of researchers easier.

Due to the extent of lines in various facilities or even residential areas, as well as the high potential of vulnerability, the safety of pipelines and compliance with the principles of risk management are of particular importance. This research was conducted with the aim of risk zoning of the pipeline route using the FAHP method and integration of RS and GIS information in the Maron oil field area of Khuzestan province. In the risk zoning of the pipeline route, after identifying the effective layers (facilities/technical and environmental), the weight of each of the criteria and sub-criteria was calculated using experts' opinions. The risk map of pipelines was estimated from technical and environmental perspectives. Then the maps obtained from environmental and technical point of views were combined to prepare the final risk-zoning map. The result showed that in the south and southeast of the region, the route of the pipelines can be more risky in case of accidents caused by the leakage of liquid petroleum products. other areas. Also, the level of risk caused by the leakage of liquid petroleum products is much higher than the leakage of gas in these areas, and it includes about 30% of the pipeline route of the oil field. According to the land use map of the region, this route passes mostly through agricultural lands, main roads, and bridges. As a result, being aware of this issue, the consequences of accidents with proper management of this issue can be controlled.

Reducing the effects of drought and optimal management of water resources is affected by the forecast of regional planners of areas affected by the risk of drought, so the evaluation and study of this phenomenon can be very important. Therefore, this study was conducted to Monitor temporal-spatial changes of meteorological drought and trend analysis of climatic variables in Khuzestan province during the period 2000 to 2020 using the SPEI index and ArcMap, XLSTAT, and RStudio software. Two non-parametric methods including Mann-Kendall and Sen's estimator slope methods were used here to analyze the annual trends of data. After calculating the drought, the effect of this phenomenon on the water area of Horul Azim and Shadgan wetlands in 2000, 2006, 2015, and 2020 was investigated. The results showed that there is a direct relationship between the changes in the wetlands cover and the problem of drought, and with the increase of drought, the water cover of the wetlands decreases and with the increase of drought, the area of water in the wetlands increases. The results also showed that the analysis of the trend of changes resulting from Sen’s slope for precipitation and temperature in the stations varied between -1.87 and 3.38 and 0.016 and 2.25, respectively. The highest slope of changes with increasing rainfall was observed in the western and central regions of the province and the slope of negative changes in rainfall has often occurred in the southern regions. Also, temperature analysis with this test showed that the temperature in all stations with a relatively slow slope in the study period had a positive and increasing trend. It is worth mentioning that the existence of increasing and decreasing trends indicates the existence of climate change, so it is necessary to study this issue and provide solutions to water resources management in times of crisis. The increasing and decreasing trend indicates the existence of climatic changes and fluctuations; Therefore, investigating this issue and providing solutions to manage water resources in times of crisis seems essential.

Road and urban transport network facilitate our daily life for optimal routing. In the road network, traffic management is one of the main challenges of managers. In this regard, the first step is to estimate the density of cars at the level of the urban road network. Estimating the number of cars or the level of occupancy of cars in the whole city, taking into account less time and cost, is only possible with satellite images. In this regard, in this research, satellite images with high spatial resolution available and downloadable from the Google Earth system have been used. To identify the position of the cars, the single shot deep learning method with RetinaNet architecture and based on residual neural networks with the number of layers 18, 34 and 50 have been used. For the training data, the position of the cars is marked with bounding boxes and then satellite images with dimensions of 128 x 128 pixels and 64 pixels pitch are cut. Of the total training data, 80% have been used for training and 20% for validation. The models were trained in 50 epochs and with an average accuracy of 0.7. Satellite images containing more than 15000 cars was used to evaluate the trained models. The parameter of the possibility of overlapping of the non-maximum suppression method was applied equal 25%. The final result shows that the use of the proposed model in the identification of cars has a good accuracy. The RetinaNet detector model based on the residual deep learning network with 50 layers has performed best in terms of average accuracy with 0.87, precision with 0.7, recall with 0.99 and F1-score with 0.82. The main challenge of the proposed models is in areas with high car density, which reduces the possibility of accurately detecting the number of cars due to the size of the ground sampling distance of satellite images, but it estimates the occupancy level better.

Automatic extraction and reconstruction of these objects from aerial and satellite data can diminish the role of humans in the production of large-scale urban spatial information and reduce the cost and time, reduce their production drastically. Identifying target objects from aerial and satellite images using computer image processing techniques and neural networks is one of the most widely used algorithms for identifying urban features. Among these types of networks are Convolution Neural Networks, which have a high power to extract high-level features in all kinds of images. In this research, more than 850 educational examples of important urban structural features including buildings, roads and single trees have been prepared. In this research, among the different types of existing networks, due to the higher speed of processing, Convolution Neural Network based on one-shot multi-box detector with ResNet network base has been used in order to automatically discover and extract the range of these complications. The proposed model is trained by training samples based on 85% training data and 15% validation data, with 120 iterations and 93% accuracy. The trained model has been implemented on different images of the study area to discover the target complications. The evaluation results of the model are based on the accuracy criterion equal to 0.86, the recovery criterion equal to 0.82, and the F1Score criterion equal to 0.83. The results show that the proposed algorithm can be recruited in fields such as producing and updating large-scale urban maps and change detection in urban areas.

Iran is one of the countries that soil erodibility is becoming one of the acute environmental problems and every year millions of tons of fertile soil are left unusable due to lack of proper management.  In order to effectively protect and prevent the adverse effects of soil erosion, it is necessary to identify the factors affecting soil erosion and provide an appropriate estimate of their amount in the country. In this regard, the present study was conducted to estimate soil erodibility (K-factor) for a soil depth of 0-30 cm in Iran.

For this purpose, two database were used, including the Harmonized World Soil Database (HWSD) and global gridded soil information (SoilGrids), as well as RStudio and ArcGIS software. First, SoilGrids data was prepared at four depths of 0, 5, 15 and 30 cm and averaging was done. Also, the HWSD database was received in vector format for a depth of 0 to 30 cm. Finally, these data have been used to estimate the erodibility factor based on the soil content of organic carbon, clay, sand and silt using the EPIC equation. Finally, Relative Error (RE), Median Absolute Deviation (MAD) and Root Mean Square Error (RMSE) were calculated to compare two databases.

Assessments indicate that the average percentage of clay particles in the sub-basins of Iran varies between 15 and 32% and the average for the whole country is 23%. On the other hand, the average percentage of silt particles in the sub-basins of Iran varies between 19 and 45%, and the average for the whole country is 32%, among which the maximum and minimum percentage of silt particles are in the sub-basins of Qarasu-GorganRoud and Kavir Lut, respectively. Also, the average percentage of sand particles in the sub-basins of Iran varies between 28 and 65, and on the other hand, the average for the whole country is 44%, the minimum of which is related to the sub-basin of Karkheh and the maximum is related to the sub-basin of Kavir Lut. In Gavkhouni and Kavir Lut sub-basins, the reason the low soil erodibility factor is the high percentage of sand in these sub-basins, so that the percentage of sand in the Lut and Gavkhouni basins is 65 and 47% of the total soil particles, respectively. Considering that the average percentage of organic carbon in the sub-basins of Iran varies between 0.3 and 3.9, respectively, these values are related to the sub-basins of Hamun-e-Hirmand and Sefidroud-Haraz, so it can be said that the majority of the country's sub-basins are in poor conditions in terms of the percentage of organic matter. The results show that the southwestern, western and northeastern parts of the country have the maximum amount of soil erodibility factor, and the central Iran and desert parts of Iran have lower erodibility due to having a higher percentage of sand particles. Also, the results show that the lowest average amount of soil erodibility at the sub-basin scale using SoilGrids data with a value of 0.033 (ton*h/Mj*mm) is related to Lut Desert and also its maximum value is 0.045 (ton*h/ Mj*mm) related to it is the sub-basin of Haleh. In addition, the maximum and minimum values of the erodibility index with the HWSD data as an average of the basins of Iran are corresponding to the Mand and Gavkhouni sub-basins, respectively, with values of 0.042 and 0.033 (ton*h/ Mj*mm). So, the results showed that the average soil erodibility factor in Iran using two the HWSD and SoilGrids databases was 0.036 and 0.038 (ton*h/ Mj*mm) respectively.

Conclusion and Suggestions:

The study of soil erodibility with the data of SoilGrids and HWSD at the sub-basins scale showed that the maximum and minimum RE in Atrak and South Balochestan sub-basins are 21 and 1 percent, respectively, and the amount of RE is about 5% for the country average; Therefore, it can be concluded that although the RE between the two databases is not high, SoilGrids data is a more suitable source for soil and water resource modeling due to its continuity and better spatial resolution. Finally, it should be said that although this database is modeled using a larger number of profiles (about 150,000 soil profiles in the world), so they have appropriate accuracy. However, it is necessary to state that investigating the uncertainty of these data in order to improve the results of this database in order to improve the results of that in different parts of the country is recommended to researchers and researchers. Also, it is noteworthy that the EPIC model was used to estimate soil erodibility in this study, while its evaluation and comparison with other soil erodibility estimation models in the country is suggested to other researchers and experts.

Changes in the quality of underground resources for various reasons, especially the unconventional use of these resources can have devastating effects on the environment.In this research, groundwater quality parameters including electrical conductivity, total soluble solids, acidity, sodium adsorption ratio, hardness, bicarbonate, chloride, sulfate, calcium, magnesium, sodium, potassium, and the sum of anions and cations were used. in this research, groundwater quality data related to piezometric water wells affiliated to the Hamadan Regional Water Company for four-year periods in 2009, 2013, 2017, and 2020 in the plains of Hamadan province have been used. The results of Moran and coefficient of gery show that the spatial distribution pattern of all water quality parameters in 2009 and 2020 is random and for the parameters of electrical conductivity and sodium adsorption ratio in 1992 and 1996, respectively, with a high level of confidence of 95% and 90%, respectively, it had a spatial distribution with a cluster pattern with high values. To investigate the spatial variations of electrical conductivity using the Kriging method, water quality maps in terms of electrical conductivity parameters for the years 2009, 2013, 2017, and 2020 have been prepared. The map of electrical conductivity changes prepared from 2009 to 2020 years shows that the amount of electrical conductivity in the city of Hamedan (Qahvand) and Razan and Kaboudar Ahang plains has more increasing changes, which indicates a decrease in groundwater quality in the case study area. Therefore, planning is necessary to correct the decline in groundwater quality.

The purpose of this study is to use modern methods to improve the accuracy of depth measurement, first by using the proposed methods based on image fusion of sentinel-2 and Landsat 8 images and then based on hybrid fusion methods. Thus, depth measurements were performed in two studied areas, namely Chabahar Bay and Mousa Estuary, based on different scenarios. These scenarios include the traditional ratio method, the proposed methods based on image fusion using three methods of Intensity-Hue-Saturation, Gram-Schmidt transformation, and Brovey transformation, and the proposed methods based on hybrid image fusion. In order to perform depth measurement based on the proposed hybrid fusion methods, the output of the image transformation using the principal component analysis method was merged with the input image of the best bathymetry map based on image fusion at another level of fusion. Chabahar Bay depth measurement results showed that in comparison with the traditional ratio model, the proposed models based on Brovey and Gram-Schmidt methods and based on a hybrid method resulting from the integration of principal component analysis output and Gram-Schmidt method have the best depth measurement results. Regarding the depth measurement of Mousa estuary, the proposed depth measurement method based on the combination of principal component analysis and Intensity-Hue-Saturation method has created the best result in comparison with the traditional ratio model. Also, the results showed that the appropriateness of the number and distribution of depth field points with the topography of the area has a great effect on the accuracy of depth measurement.

Studying the changes and destruction of resources in previous years, and possibility-evaluation and prediction of these changes in subsequent years, could be a significant step in the planning and efficient use of resources, and controlling and containment of the unprincipled changes in future. This study aims to assessment and predicting changes in land use and land cover in the city of Ahvaz. In doing so, Remote Sensing Data, including Landsat TM satellite images of the years 1997, the ETM+ of 2009 and OLI image of 2021 have been used. After accomplishing the needed preprocessing, satellite images were processed and classified through choosing the appropriate training areas. For the Change Assessment, post-classification comparison method was used, and tables and maps of changes were prepared. General changes in the 24-year period is such that the space of built-up areas, with 6 square kilometers rate of growth per year, have been increased from 100/27 square kilometers to 230/33 square kilometers. The extension of the countryside had been associated with the destruction of Barren lands and agricultural, and the agricultural usage had been increased from 951/52 square kilometer in 1997 to 957/26 in 2021. Modeling has been performed through using CA-Markov models. These results are demonstrating the appropriate ability of CA-Markov model in modeling and predicting changes. Thereafter, the land cover map for 2033 was simulated with CA-Markov model.Studying the changes and destruction of resources in previous years, and possibility-evaluation and prediction of these changes in subsequent years, could be a significant step in the planning and efficient use of resources, and controlling and containment of the unprincipled changes in future. This study aims to assessment and predicting changes in land use and land cover in the city of Ahvaz. In doing so, Remote Sensing Data, including Landsat TM satellite images of the years 1997, the ETM+ of 2009 and OLI image of 2021 have been used. After accomplishing the needed preprocessing, satellite images were processed and classified through choosing the appropriate training areas. For the Change Assessment, post-classification comparison method was used, and tables and maps of changes were prepared. General changes in the 24-year period is such that the space of built-up areas, with 6 square kilometers rate of growth per year, have been increased from 100/27 square kilometers to 230/33 square kilometers. The extension of the countryside had been associated with the destruction of Barren lands and agricultural, and the agricultural usage had been increased from 951/52 square kilometer in 1997 to 957/26 in 2021. Modeling has been performed through using CA-Markov models. These results are demonstrating the appropriate ability of CA-Markov model in modeling and predicting changes. Thereafter, the land cover map for 2033 was simulated with CA-Markov model.

Particulate matter is one of the main air pollutants in urban areas, which are usually produced from various sources such as urban vehicles, fossil fuels, industrial activity and cause respiratory diseases, cardiovascular disease and death, therefore, it is very important to be aware of spatial changes in this pollutant in areas with high levels of pollution. In this regard, the present study was conducted with the aim of spatio-temporal evaluation of the PM2.5 index in the period 1998 to 2016 in Khuzestan province.

For this study, in first precipitation, Land surface temperature (LST), wind speed, Digital Elevation Model (DEM) and vegetation cover parameters were prepared using four satellites Terra, Landsat 8, SRTM and GPM and ground data. Then PM2.5 index for four periods of 1998, 2004, 2010 and 2016 was extracted using satellite products for Khuzestan province. Also, information on the distribution of the population and industries of the province was received from the relevant organizations. Finally, after providing the spatio-temporal changes of PM2.5 index in Khuzestan province, the spatial changes of this index were studied with the mentioned parameters to evaluate the effect of each of these parameters on the pollution of this index.

The results of the present study in Khuzestan province showed that the southern cities of the province such as Mahshahr, Abadan and Shadegan are regions with higher potential in terms of particles smaller than 2.5 microns in size. The results of the study of population density and industries of this province showed that most of the cities in which the air pollution rate due to PM2.5 index was high, had more industries and population density. The results also showed that in all study periods, in the northern and northeastern parts of the province, the amount of air pollution caused by this index was much lower than other regions of the province and the reason for this could be the low density of industries and population of these cities, among which we can mention the cities of Lali and Indika. In addition to the direct relationship between industry and human activities in increasing and decreasing the concentration of PM2.5 index, the relationship between this index and several factors (DEM, wind speed, precipitation, temperature and vegetation cover) was investigated. The correlation results between the mentioned parameters and PM2.5 concentration showed that the highest correlation is between PM2.5 concentration and precipitation and this relationship is inverse.

Considering the mentioned cases in general, it can be said that the concentration of PM2.5 pollutants in the southern and central areas is much higher than other areas and this could be due to the high density of power plants, industries and vehicle pollution in these areas. In addition, environmental and climatic factors can play an important role in the persistence and spread of the air pollution layer of this index. It should be noted that this research can be the basis for decision-making on-air pollution management, which is especially important in the management of air pollution crises.

Due to the necessity of identification and need assessment phase in the creation and the development of Geodatabase and Geographic Information System (GIS), designing a concept model is being introduced as its first phase of the study. Conceptual data modeling using ERD (Entity-Relationship Diagram) method, by defining entities, attributes, and the relationship between spatial and non-spatial entities. In this study, using the ERD method, a spatial database structure is designed for geochemical data of oil wells. Petroleum geochemical data is used for oilfield exploration, management, production, and development. In an oilfield, the most basic spatial entity is the wells. Other spatial entities in this study are field boundaries, sectors, and formations. Non-spatial entities were defined in separate tables, including oil samples and parameters derived from the geochemical analysis of samples in the Gachsaran oilfield. The ER diagram was designed using SAP Power Designer Software by establishing a 1 to N relationship between the well and all entities as well as determining the primary key and foreign key for each relationship. 11 entities were identified to create the data model.The result of applying the conceptual model before creating the Geodatabase is the logical structure of the database; also, spatial data modeling ensures the success of creating and developing a Geodatabase and WebGIS. As a result, the designed conceptual model simplifies the creation of a spatial database for display, processing, query and analysis of geochemical data of oil wells.

Khuzestan province is one of the largest industrial provinces in Iran, with high air pollution. One of the main air pollutants is nitrogen dioxide (NO2) in the atmosphere, which is linked to several epidemiological and environmental effects. Thus, spatial and temporal monitoring of NO2 is crucial for land managers. So, the aim of this study was the spatiotemporal evaluation of NO2 in Khuzestan Province, Iran, using Sentinel 5 (TROPOMI).

Initially, the amount of tropospheric NO2 concentration was estimated in each month. In the next step, using ArcMap software, the monthly average of tropospheric NO2 was extracted from 12 months. Moreover, the location of 100 important air pollutant industries in the Khuzestan Province was extracted using Google Earth image. Thus, using the monthly average NO2 concentrations and the location of the cities and industries, the monthly average pollution of this pollutant was extracted for the cities and industries. Finally, the cities and industries of air pollution in Khuzestan Province were prioritized based on of tropospheric NO2 concentration.

The maximum concentrations of this gas was in Bavi, Ahvaz, Abadan, and Mahshahr cities; and respectively, this gas had the lowest values of NO2 in Izeh, Lali, and Baghmalek cities. Moreover, in the cold months of the year, Khorramshahr and Mahshahr had higher concentrations of NO2 in comparison to the warm months of the year. The results of the spatial analysis revealed that the highest concentration of NO2 was in the Farabi Petrochemical Company, Khuzestan Steel Company, Ghadir, and Razi Petrochemicals, respectively.

According to the findings of this study, it can be deduced the influence of local emission sources of NO2 in Khuzestan Province is related to population density, high traffic of motor vehicles, and industrial activities.

Assessment of data uncertainty increases our ability to study and accurate understanding of about real world, because awareness from uncertainty of resources and remove error for improving the obtained results is one of the most important items in the remote sensing science. Since the digital elevation model (DEM) extracted from different sensors and geometry, Therefore selection of the most suitable DEM that provide better ground conditions is the one of the main needs for experts in this field. The aim of this study to vertical accuracy assessment of the ASTER and SRTM digital elevation model compared to the elevation data from topographic maps that prepared by the Iran National Cartographic Center in the 12 city from Khuzestan province. In order to evaluation the accuracy of the data, the Mean Error (ME) index and Root Mean Square Error (RMSE) index are used. The result of evaluation ME index for the cities with topographic map for ASTER and SRTM data showed the value 6.08 and 2.34 meter, respectively. Also, the result of RMSE index for ASTER and SRTM data showed the value 6.94 and 3.46 meter, respectively. Therefore more, the result showed that in most cities of Khuzestan province the DEM derived from SRTM has a higher accuracy than the ASTER digital model; Since the SRTM data have a low uncertainty, Therefore in studies related to the extraction drainage network, drainage density, physiography of the watershed area, runoff or geology, it is suggested that SRTM elevation model be used.

Evapotranspiration is one of the most important factors in the hydrology cycle and is one of the determinants of energy equations at ground level and water balance. Most of the ground-based methods use point measurements to estimate evapotranspiration. Remote sensing has the ability to estimate the amount of evapotranspiration and examine its spatial distribution. In this study, Landsat8 and Sentinel2 satellite images combined to estimate the actual daily evapotranspiration of sugarcane in the Mirza Kouchak khan Agro industrial Company, Khuzestan province, using the SEBAL model at six dates.Validation of SEBAL model performance was performed in two modes: using integrated images and using Landsat 8 images with lysimeter data. The results indicated that the SEBAL model with Landsat 8 satellite images with (R2=0.88), and the SEBAL model with Landsat 8 and Sentinel2 satellite images with (R2=0.90), Overall, it was well correlated with the lysimeter method and estimated similar and appropriate results.

Given the importance of surface water for drinking, agriculture, and industry, the protection of these resources against pollution is of great importance for national and regional health.
The integration of satellite imagery to produce data with a higher information level is called image fusion. Fusion methods are generally divided into three categories: pixel-based methods, feature-based methods, and decision-based methods.The main objective of this research is to optimize seasonal zoning of the water quality indices of Karoun River based on two indices of NSFWQI (National Health Foundation Water Quality Index) and IRWQIsc (Iran Surface Water Quality Index for Conventional Pollutants) and therefore, the fusion of Sentinel-2 and Landsat 8 images will be evaluated. It is noteworthy that based on our knowledge the evaluation of image fusion methods in the field of qualitative indices zoning has not been conducted yet.

Measured Quality Parameters Seven sampling stations were selected for seasonal measurement of water quality parameters along a part of the Karoun River in 2018. The measured qualitative parameters included chemical oxygen demand, biological oxygen demand, dissolved oxygen, electrical conductivity, fecal coliform, PH, nitrate, phosphate, total hardness, and turbidity.
 NSFWQI NSFWQI was introduced by the US National Health Organization in 1970. In this index, several questionnaires were filled out by experts in the United States, which are the basis for adjusting this index. Based on the answers, a curve was plotted for each parameter to determine the sub-indices of each parameter.
 IRWQIscTo evaluate the quality of rivers and compare their pollution rates in Iran concerning natural conditions and water resources problems, an index (IRWQIsc) was introduced to provide a good perspective about the quality status of water resources in Iran.
 Utilized satellite imageries Four pairs of geometrically and atmospherically corrected Sentinel-2 and Landsat-8 images were used to seasonally map the water quality indices in the Karoun River.

Calculation of NSFWQI quality index and its seasonal zoning Using the measured field qualitative parameters and equation, the NSFWQI parameter value was calculated. Next, by calculating the CR value between the input images and this index, optimal regression models based on the maximum CR values were developed to seasonally map the entire region.

Calculating the IRWQIsc and its seasonal zoning: Based on the measured qualitative field parameters, the value of the IRWQIsc index was calculated. Next, by calculating the CR value between the input images and this index, optimal regression models based on the maximum CR values were developed to seasonally map the entire region.

Comparison of the output maps of NSFWQI and IRWQIsc indices showed that Karoun River's quality status is inappropriate during most of the year and in most areas and it is best to use it with water treatment. According to the NSFWQI index in the NSFWQI2 map of the spring, the qualitative status of the study area changes from bad to moderate and to bad again considering north to south direction. In the summer NSFWQI2 map, the quality of the Karoun River changes from bad to moderate. In the autumn, based on the NSFWQI1 map, the qualitative status changes from bad to medium and again to bad. The qualitative status of the river in the winter shows moderate status. A survey of the qualitative status of the study area based on the IRWQIsc index also shows that in the spring, the river status changes from bad to relatively bad and again to bad. The qualitative change of the region in summer based on the IRWQI2 map changes from bad and relatively bad to relatively good and finally to relatively bad. In the autumn, the IRWQI1 map shows the change from bad to relatively bad and again to bad. In winter, according to the IRWQI2 map, the quality of the Karoun River changes from relatively good to relatively bad.

Considering the progress made in remote sensing technology, collecting information on the quality of surface water resources by this technology, while reducing the cost and time of traditional sampling, can monitor all surface water zones. In this study, the Sentinel-2 satellite images were used to estimate the concentration of acidity, bicarbonate and sulfate parameters. Initially, Sentinel-2 satellite images were pre-processing and then bands and spectral indexes were determined to identify the significant relationship between the parameter values of water quality and images using the multivariate regression method. In the next stage, using Artificial neural network (ANN) and Adaptive Neuro fuzzy inference system (ANFIS) models, the relationship between Sentinel-2 satellite images and water quality parameters were modeled and then their accuracy was calculated for real values. The results showed that in the modeling of sulfate parameter using Sentinel-2 satellite, ANFIS model with relative error equal to 0.0773 and RMSe equal to 0.8014 has a higher accuracy compared to ANN models with relative error equal to 0.1581 and RMSe equal to 1.2477. While, the relative error of the results of the ANN model are obtained 0.0064 and 0.0556 for acidity and bicarbonate parameter, respectively, and RMSe is equal to 0.0702 and 0.2691, respectively.  The ANFIS model has a relative error of 0.0165 and 0.0722, and RMSe is 0.1975 and 0.3037 for acidity and bicarbonate parameter, respectively. Finally, using satellite images, the mentioned models were applied to prepare a qualitative map of each parameter along the part of the Karun river.

 Considering the importance of rivers as part of freshwater resources and their role in meeting the needs of agriculture, industry, urban populations, etc., monitoring and predicting the quality of these water resources is essential. These water sources are affected by numerous factors due to their different geological and environmental conditions and their qualitative status also undergoes dramatic changes. However, the quality monitoring of these abundant water resources on the planet's surface is not feasible and requires the use of advanced and powerful tools (Bagherian Marzouni et al., 2014). Due to its capabilities, satellite remote sensing can be used as one of these tools in monitoring water quality and will accurately detect the spatial and temporal changes of these water sources (Bonansea et al., 2015). So far, in many studies, the capabilities of remote sensing satellites to estimate surface water quality parameters has been evaluated, and in most of them, acceptable results have been obtained indicating the ability of this technology in the issue as mentioned above. Among these studies, we can mention laili et al. (2015), in which in a small section of Indonesian waters, have figured out a new regression algorithm between Landsat 8 and groundwater quality parameters. Toming et al. (2016) in a study using satellite images of Sentinel-2 on the water quality of the lakes in Estonia, could find a good correlation between the satellite band proportions and ground. The purpose of this research is to establish a relation between satellite images of Sentinel-2 A and two quality water parameters with a suitable model along the Karun and Dez River. For this purpose, firstly suitable spectral indices were extracted from them by applying the necessary processing on satellite images. In the next step, optimal relationships between extracted indices and water quality parameters are established using different models. Finally, using models with higher accuracy in terms of modeling, the dispersion map of each parameter in the length of the Karun River is provided.The purpose of this research is to establish a relation between satellite images of Sentinel-2 A and 2 quality water parameters with a suitable model along the Karun and Dez River. For this purpose, firstly suitable spectral indices were extracted from them by applying the necessary processing on satellite images. In the next step, optimal relationships between extracted indices and water quality parameters are established using different models. Finally, using models with higher accuracy in terms of modeling, the dispersion map of each parameter in the length of the Karun River is provided. 

This study presented in eight steps as below: Step 1: Preparation of ground data and satellite imagery:The ground data used in this study is the measured data at the water quality sampling stations. The data included information on these quality parameters that were used from 2015 to early 2017 in ten stations. Step 2: Recording the value of the reflection bands at the ground measurement stations:In order to implement this research, satellite images of sentinel-2 and groundwater quality parameters were collected and measured at the same time from the study area. In this step, the values of measured water quality parameters were also sorted by date and sampling stations were prepared in separate files. Step 3: Analyze the initial sensitivity and determine the bands that have a stronger connection with each water quality parameter  

Table 1: result of sensitivity analysis for sentinel-2 bands TDS Turbidity EC pH Hco3 So4 Cl Na K Mg Ca Parameter Type Band Number 0.376 0.472 0.296 0.384 0.493 0.219 0.338 0.279 0.312 0.217 0.294 B2 0.379 0.303 0.325 0.307 0.238 0.239 0.268 0.238 0.179 0.291 0.217 B3 0.352 0.237 0.283 0.278 0.260 0.232 0.225 0.269 0.165 0.196 0.269 B4 0.346 0.332 0.274 0.428 0.315 0.214 0.256 0.294 0.256 0.275 0.313 B5 0.401 0.208 0.248 0.322 0.294 0.278 0.253 0.249 0.210 0.268 0.239 B6 0.403 0.257 0.227 0.299 0.273 0.281 0.258 0.256 0.203 0.283 0.210 B7 0.263 0.285 0.301 0.346 0.198 0.245 0.231 0.227 0.184 0.209 0.224 B8 0.422 0.306 0.316 0.309 0.241 0.275 0.251 0.244 0.195 0.299 0.212 B8a 0.249 0.205 0.267 0.325 0.238 0.273 0.233 0.287 0.205 0.209 0.158 B11 0.391 0.265 0.214 0.310 0.282 0.293 0.254 0.247 0.270 0.244 0.178 B12

  Step 4: Calculating spectral indices and selecting spectral indicators with higher correlation Step 5: Secondary Sensitivity Analysis and Selection of Spectral Indicators with Stronger Connections   In the next step, by applying the sensitivity analysis method, the relationship between each spectral indicator and water quality parameters was calculated (Table 2).

  Table 2. Result of sensitivity analysis for spectral indicator TDS Turbidity EC pH So4 Hco3 Cl Na K Mg Ca Parameter Type   Spectral Indexes 0.455 0.580 0.470 0.407 0.534 0.260 0.482 0.535 0.364 0.511 0.366 Single bans reflectance 0.465 0.659 0.563 0.516 0.599 0.501 0689 0.688 0.670 0.532 0.666 ( 14BmaxBmin)"> 0.436 0.740 0.452 0.633 0.562 0.681 0.701 0.598 0.600 0.485 0.677 ( 14BminBmax)"> 0.396 0.702 0.438 0.720 0.527 0.581 0.758 0.669 0.656 0.506 0.740 ( 14Bmax-BminBmax+Bmin)">   Step 6: Normalization of data Step 7: Modeling the relationship between satellite images and groundwater quality parameters:In order to model the relationship between satellite images and groundwater quality parameters, and based on the results of previous steps, the normalized values derived from the calculation of spectral indices were determined as inputs and water quality parameters were determined as outputs of ANN and ANFIS models. Step 8: Providing water dispersion map for water quality parameters:At this step, the modeling process was repeated with the transformation of ANN and ANFIS models until each model was accurately mapped the relationship between water quality parameters. 

Table 3 shows the evaluation result of used model in this study.   Table 3. Evaluation result of ANN and ANFIS model for water quality parameters. Hco3 So4 Cl Na K Mg Ca WQPT ANFIS ANN ANFIS ANN ANFIS ANN ANFIS ANN ANFIS ANN ANFIS ANN ANFIS ANN Error Type 0.497 0.315 0.0871 0.691 0.266 0.263 0.229 0.264 0.136 0.0709 0.127 0.397 0.120 0.279 RE 0.164 0.131 0.0587 0.311 0.0959 0.0748 0.102 0.079 0.126 0.0605 0.077 0.157 0.115 0.194 RMSE   Figures 1– 4 show the concentration map of TDS and turbidity parameters studied in this research in Karun River in Dez and Karkheh dam and the Karun River from Malasani section to the Farsiat station.                                  (a)                               (b) Figure 1: Concentration map of TDS parameter in a) Karkheh and b) Dez dam.   Figure 2. Concentration map of TDS parameter Karun River.     (a)   (b) Figure 3. Concentration map of turbidity parameter in A) Karkheh and b) Dez dam.   Figure

map of turbidity parameter Karun River.   4- Conclusion In this study, two models of ANN and ANFIS computational intelligence models were used to model the relationship between satellite images of Sentinel-2 and two quality parameters of water along the Karun River. The results of this study indicate the high level of remote sensing ability to monitor water quality, similar to other studies; as this is well understood in previous researches, remote sensing technology can be widely used to monitor other surface water resources of Khuzestan province.   References Bagherian.Marzouani.M., Akhoundali.A.M., Moazed.H., Jaafarzadeh.N., Ahadian.J., Hasoonizadeh.H., 2014, Evaluation of Karun River Water Quality Scenaros Using Simulation Model Results, International Journal of Advanced Biological and Biomedical Research, Volume 2, Issue 2, 339-358. Bonansea.M., Claudia Rodriguez.M., Pinotti.L., Ferrero.S., 2015, Using multi-temporal Landsat imagery and linear Models for assessing water quality parameters in Rio Tercero reservoir (Argentina), Journal of Remote Sensing of Environment, 158, 28–41. Laili, N., Arafah, F., Jaelani, L., Subehi, L., Pamungkas, A., Koenhardono, E., & Sulisetyono, A. 2015. Development of water quality parameter retrieval algorithms for estimating total suspended solids and chlorophyll-a concentration using landsat-8 imagery at poteran island water. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(2), 55. Toming, K., Kutser, T., Laas, A., Sepp, M., Paavel, P., Nõges T., 2016. First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery, Remote Sensing journal, 8, 640.

In order to increase the spectral and spatial resolution of satellite images, the fusion of multi-resolution images can be used, however, the image fusion usually introduces some spectral and spatial distortions in the resulting fused image that the amount of these distortions highly depends on the pan sharpening technique as well as the type of data. The aim of this study is the fusion of Landsat 8 Operational Land Imager (OLI) with the date of 8 January 2018, and Sentinel-2 Multispectral Imager (MSI) with the date of 11 January 2018, using the Gram Schmidt, CN, Brovey, DWT, SFIM, Multiplication methods in the Ahvaz city. In order to evaluate the image quality that fussed, CC, SAM, PSNR, SIMM, and Laplacian indices were used. The results showed among the used methods, DWT and Brovey, have the best results from the spectral and spatial aspect, respectively. Also, the highest spatial distortion with the amount of 1.18 in the SAM index was in the Multiplication method and the maximum spatial deviation in the DWT index with a correlation of 0.47 with the reference image. Multiplication and DWT methods have the most spatially and spectrally distortion. Therefore, the results showed that SFIM is the optimum method of spectral and spatial aspects.

The tourism industry has become a major economic activity in the early years of the 21st Century and is considered one of the most productive and most employment-oriented global industries. Tourism is one of the most important factors generating wealth and employment in the world. It is necessary to plan for the proper exploitation of this industry, The most important steps to plan are to locate sites for providing tourists with the services they need in the form of tourist villages, This research is for the city of Shush which is one of the most important tourist areas of Khuzestan province And since it has many ancient monuments, it has attracted many tourists, , But the city has been at a very low level in terms of having a space worthy of tourists. Therefore, the conditions reinforced the idea of creating a tourism village. In this research, location-based discussion was conducted through a fuzzy inference system, Finally, the Fuzzy Topsis method has been used to protect the environment and to some extent extend sustainable tourism development. The ranking of these sites is based on environmental criteria. In the fuzzy inference system by applying the layers required in this method, four sites are considered to be very suitable.Then, using Fuzzy Topsis, which includes 10 criteria and 4 options, identified the best site on site 4. This site will bring the least damage to the environment, Located on the banks of the Dez River, most of the area has been covered by ground. In terms of maintaining environmental criteria, the site has a completely organic environment than other sites.

Emission reduction of carbon dioxide and its impact on global warming process is one of the most important results in the use of renewable energy sources. In urban areas, solar systems stationed on building roofs are the most appropriate utilization method of stable source of solar energy. Urban compactness is a major urban form parameter that affects the accessibility of solar renewable energy in the built environment, so it is essential that the effects of urban compactness on buildings utilization of solar energy, be surveyed. The aim of this study is the evaluation of solar energy potential in urban areas and determination of the relation between urban compactness indicators and solar energy potential in the local scale. In this study, the annual solar radiation modeling for each of the building surfaces of the study area, over one year period, was performed using the Solar Analyst model. On the other hand, the relation between various compactness indicators including site coverage, plot ratio, volume-area ratio, Entropy, population density and building density with Annual solar radiation received by the study area was explored and using least squares regression model, the relationship between the solar radiation and urban compactness indicators was determined. Also potential of using two types of active solar systems including Building Integrated Photovoltaic Systems (PV) and Solar Thermal Collectors (STC) was evaluated. According to the results, the annual solar irradiation increase from 507 (in compression areas) to 741 (in scattered areas) Kilowatt hours per square meter. Volume-area ratio shows the highest determination coefficient, R2 equal to 0/805 with annual solar radiation. Also in the study area, the building roofs potential for the development of PV systems much more than STC systems, influenced by the degree of urban compactness.

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

Abstract
One method of flood forecasting and flood control in rivers is ‘flood routing'. The relationship between precipitation and runoff and creating flooding in the region is not linear mathematical relationship which we can predict flooding in one region and such phenomena should be regarded as a model. Artificial intelligence methods such as artificial neural network and fuzzy inference system can be used as a good method in this field. In this study, using artificial neural network and fuzzy inference system, which are two types of the most widely used computational intelligence, we attempt to predict flood in Zard River. For the implementation both methods, first, the necessary data were collected and then wrong data were excluded from the data set and the data have been normalized. Modeling using artificial neural networks using MATLAB software coding was performed on data. To implement, the fuzzy inference system were used from prepared data. In this study, types of artificial neural networks structures with different number of neurons and hidden layers, number of educational courses and different functions have been performed on the data until obtaining the best structure for study area. Fuzzy inference models were implemented until the best model is chosen. Results showed that in general, fuzzy inference system have a better simulate data in the studied area and better and more accurate results than the artificial neural network model is showed. Also, values of MSE and r in fuzzy inference system and artificial neural network is equal to 0.2196, 0.0297, 0.7667 and 0.96 respectively which shows higher accuracy of fuzzy inference system for predicting floods in the our area of the study.

Groundwater pollution is a complex and full of uncertainty process, on a regional scale. Development of an integrated method for assessing groundwater vulnerability, can be efficient in order to optimized management and protection of them. Because of fertile soil and sufficient water resources, Ramhormoz plain is suitable area for agriculture that by development of agriculture, use of chemical fertilizers and pesticide, this plain always is at risk of contamination. One of the suitable approach to prevent groundwater contamination, identify areas of potential contamination. The aim of this study is to produce vulnerability map of Ramhormoz plain alluvial aquifer using DRASTIC model, and then use artificial intelligence techniques to improve the results of the DRASTIC model. Due to the importance of groundwater resources in the study area that are used for various purposes including agriculture, Aquifer vulnerability study and protect these areas for development and management of water resources is essential.
In this study, first, vulnerability evaluation of Ramhormoz alluvial aquifer plain was performed using DRASTIC model and in the following, artificial intelligence methods was used to optimize the model. DRASTIC model includes the following parameters: depth to water table, net recharge, aquifer media, soil media, topography, impact of vadose zone and hydraulic conductivity that are effective in groundwater vulnerability assessment. This method, based on the standard weights of DRASTIC model and obtained layers for each of the seven parameters, calculates the amount of aquifer vulnerability. After preparation of the layers, vulnerability of Ramhormoz alluvial aquifer plain was determined using drastic model. Also the groundwater vulnerability map and DRASTIC index was calculated for the entire area. In order to evaluation of accuracy of the obtained results from the model, nitrate concentration data existing in groundwater have been used for verification. Following In order to improve results, DRASTIC model was integrated by artificial neural networks, fuzzy logic (Sugeno and Mamdani) and Adaptive Neuro-Fuzzy Inference System methods and four vulnerability maps was obtained using different models of artificial intelligence.
the groundwater vulnerability map toward the contamination was prepared by the division into three vulnerability ranges including low, medium and high and DRASTIC index was calculated for the entire area between 48 and 156. Correlation coefficient 0.97 between DRASTIC index and nitrate concentration reflects the relatively good accuracy of this method. Also, the results of the implementation of these models showed that the used artificial intelligence models have the ability to improve the primary DRASTIC model results. By comparing the results of the models can be concluded that the Adaptive Neuro-Fuzzy Inference System model has the best result.
The determination coefficient, R2, for the Adaptive Neuro-Fuzzy Inference System, neural networks and Mamdani fuzzy and Sugeno fuzzy models, is 0.99, 0.94, 0.98 and 0.87 respectively. According to the final model, South- Southeast regions have the highest potential for contamination.

Several modelling techniques have been developed for habitat suitability modelling. In the meantime, the Fuzzy Inference System (FIS) with ability to model uncertainty of input variables is an effective method to model wildlife species habitat suitability. So, Persian Leopard habitat suitability was predicted in Shimbar Protected Area using FIS. Therefore, the effective environmental variables were determined. We also defined and determined the linguistic variables, linguistic values, and range of them. Then, we designed the membership functions of the fuzzy sets of the input and output variables. Also, the definition of the fuzzy rules in the system was performed. Finally, the defuzzification of output was carried out. The accuracy of the predictive model was tested using AUC. Also, 11 FISs were developed to determine sensitivity of the models and important variables in modelling. The results showed that the predictive model was more efficient than the random model (AUC=0.960). In addition, the ‘distance to capra’ was the most important predictor. According to the success of FIS in Persian Leopard habitat suitability modelling, we suggest this method to improve and complete the existing spatial information of wildlife habitats in Iran, especially about regions and species that have been less studied.

Status determination of wildlife habitats is very important in conservation programs and management of wildlife. So, in this study Ursus arctos habitat suitability was modeled using maximum entropy algorithm (MaxEnt) in Shimbar protected area. In order to model the habitat suitability, after investigating and resolving the spatial autocorrelation of occurrence records, spatially independent localities were divided into the calibration and evaluation sets and then were combined with 10 environmental variables (VIF