Journal of Spatial Analysis

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Zoning the possibility of landslides downstream of Sanandaj Dam
1-Introduction
The purpose of this study is to select the best model and identify landslide risk areas in the downstream basins of Sanandaj Dam. Every year, mass movements in the region cause damage to roads, power lines, natural resources, farms and residential areas, and increase soil erosion. Kurdistan province, with its mostly mountainous topography, high tectonic activity, diverse geological and climatic conditions, has the most natural conditions for mass movements. According to the available statistics, this province is the third province in terms of landslides after Mazandaran and Golestan. (Naeri, &Karami, 2018). The Gheshlagh River Basin is a mountainous region with a north-south trend. In terms of construction land, it is located on the structural zone of Sanandaj-Sirjan. The study area with an area of 970.7 square kilometers is located downstream of Sanandaj Dam. The city of Sanandaj is being studied within the region. Due to the type of climate and morphological processes, effective parameters are provided for landslides in the geography of the region.
2-Methodology
In this study, 9 effective factors for landslides, including slope, slope direction, fault distance, road distance, waterway distance, lithology, land use and precipitation were used. Using Google Landsat 8 ETM satellite imagery, Google Earth software identified 237 slip points. Then, the coordinates of the slip points were transferred to the Arc GIS software and a map of the landslide distribution area in this environment was prepared. Also, in this study, 89 non-slip points were prepared for use in the training and testing stages of Persephone neural network inside slopes less than 5 degrees. Artificial neural networks are made up of a large number of interconnected processing elements called neurons that act to solve a coordinated problem and transmit information through synapses. Neural networks begin to learn using the pattern of data entered into them. Learning models, which is actually determining their internal parameters, is based on the law of error correction. In this method, by correcting the error regularly, the best weights that create the most correct output for the network are identified. The neurons are in the form of an input layer, an output layer, and an intermediate layer. ahp includes a weighting matrix based on pairwise comparisons between factors and determines the level of participation of each factor in the occurrence of landslides. In this model, a large number of factors can be involved and the weight of each factor can be obtained using expert opinion.
3-Results
According to the results of the high-risk class neural network model, which occupies 31% of the basin area, it is the widest risk zone in the region. The middle class also accounts for more than 29 percent of the area, followed by the low-risk class. The results of the AHP model show that the middle class, with 32% of the area, has the highest dispersion in the region, the low-risk class and then the high-class are in the next position. The AHP model was used to prioritize the parameters affecting the landslide. The parameters of slope, lithology and land use play the most important role in the occurrence of landslides, respectively, and have the least role for slope direction, distance from fault and height. The results of the models used are consistent with the reality of the region's wide-risk hazards, and high-risk areas based on the models used are mostly located in the west and southwest of the basin. These areas correspond to the mountain unit and the steep slope. Based on the results of AHP model, the impact of human factors in the occurrence of landslides is weaker than the natural factors of the region and human factors play a stimulating and aggravating role in primary factors. Five methods for error detection were used to evaluate the models used
4-Discussion and conclusion
 .Due to the sensitivity of unstable slopes in the region, any planning to change the use and construction that increases the weight of the load on unstable slopes should be done in terms of geomorphological and geological conditions of the region.
Keywords: hazard zoning, landslide, neural network, AHP. Sanandaj Gheshlagh Watershed
 

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The role of environmental and climatic environment on the transport and emission of carbon monoxide pollutants Iran in 2018
 
Introduction
Air pollution, as one of the most important environmental hazards in urban areas, is closely related to weather conditions. Today, pollution in metropolitan areas has become an important issue that requires the study and presentation of practical solutions to improve living conditions in this area. Therefore, understanding the relationship between synoptic systems and air pollutants helps a lot in how to solve environmental problems and future planning. Therefore, in this study, compression algorithms of carbon monoxide emission and transfer from domestic and foreign sources were analyzed. For this purpose, GEOS-5 / GMAO / NASA satellite images were used. The results showed that the highest amount of pollution from the seasonal point of view is related to the cold and early morning seasons and the lowest is related to the early afternoon and hot season of the year. And Khuzestan are densely populated carbon monoxide cores. Low pressures of the eastern Mediterranean play an important role in reducing pollutants in the southwest of the country and in the south of the country, under the influence of atmospheric currents from the topographic cut of Bandar Abbas, air streams polluted with carbon monoxide are able to penetrate into the interior to the southern half of Kerman. Increased by low pressure systems in Afghanistan and Pakistan. The Zagros Mountains also play an important role in preventing the entry of pollutants produced by western neighbors into Iran. In summer, Iran is polluted by carbon monoxide carriers by monsoon currents from central and southern Africa to Iran and has caused a lot of pollution.        
                                                       
materials and Method
The geographical location we study in this study is Iran. Iran is the 16th largest country in the world. Iran is located in the northern hemisphere, the eastern hemisphere in Asia and in the western part of the Iranian plateau and is one of the Middle Eastern countries. Meridian 5 44 passes east of the westernmost point of Iran and meridian 18 63 passes east of the easternmost point of Iran. 1648195 sq km is bordered by Armenia, Azerbaijan, and Turkmenistan to the north, Afghanistan and Pakistan to the east, Turkey and Iraq to the west, the Persian Gulf and the Sea of ​​Oman to the south. Iran is one-fifth the size of the United States and almost three times France. . Iran is a mountainous country. More than half of the country is covered by mountains and heights, and less than 1/4 of it is arable land. In general, Iran's heights can be divided into four mountain ranges: North, West, South and Central Mountains. East divided, which is therefore the twenty-third highest mountain in the world.                                        
This study is based on the method of environmental analysis to focus on circulation, so that based on the concentration of carbon monoxide in 2018, synoptic patterns of this phenomenon have been identified. Satellite imagery of surface carbon monoxide was then obtained from three GEOS-5 / GMAO / NASA organizations. Also for synoptic analysis, MSLP and WS satellite images were received and analyzed from GFS / NCEP / US National Weather Service organizations and also one of the sensors used for pollutant studies is MOPITT. The MOPITT sensor is a tool for measuring troposphere pollution that can detect atmospheric pollution. This sensor is the first satellite sensor designed for use in gas correlation spectroscopy and is part of NASA's Operational Program (ESE), which has been operating since 1999 and is installed on three satellites Terra, Aura, Aqua Depending on the type of mission in space, it acts as an orbiter. This sensor measures only two variables of methane and carbon monoxide in the atmosphere of the troposphere of the atmosphere, for which purpose 3 bands and 8 channels for measuring monoxide with a size of 62.4 microns (using 4 channels), 33.2 It uses microns (using 2 channels) and methane measuring 26.2 microns (using 2 channels). The MOPITT sensor is specifically designed to measure carbon monoxide. The geographical boundaries of the study area were also selected to include all atmospheric systems affecting the study area.     
                                                                                                                                    
Conclusion
The meteorological condition and the physical and dynamic properties of the atmosphere can play an important role in the level of air protection. The main factor that can cause the scattering and transmission of air forces is the use of the ground and the levels of reception of the atmosphere, and the synoptic systems as a service provider providing services for upward movement and distribution of air pollutants, as well as the definition of chalk. As a decision made in this field, Iran can use its images in this field in 2018 2018, MSLP, WS will provide you with GFS / NCEP / US National Weather Service. With great intensity you can go to Tehran and southwest to destroy yourself and access your officials. In the imagination carbon monoxide is possible and used in the southwest of the country. Now in your country and change the status of lists proposed by Coriolis, increase the high pressure of carbon monoxide in Mr. Tropical from the Middle East and Iran. This program allows you to modify your suggested lists. Carbon monoxide pollutants sent to a drawer in the international province of the country and available in Bandar Abbas, a road nest free from high mountains and as a corridor company you can get from this par of the air pollution as carbon monoxide through the air to this one Use the land up to the Kerman province.          
                                                               
Keywords: Carbon monoxide, Compression systems, Monson, Atmospheric pollution, Topography
 

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Regional leveling of drought vulnerability in Golestan province
Narges Kefayati*¹-  Khalil Ghorbani²- Gholamhossein Abdollahzadeh ³-
 
1- PhD student of irrigation and drainage, Department of Water Engineering, College  Of Water   Engineering, Gorgan University of Agricultural Sciences and Natural Resources,Gorgan,Iran. (Corresponding Author)*
2- Associated Professor, Department of Water Engineering, College  Of Water   Engineering, Gorgan University of Agricultural Sciences and Natural Resourcesm, Gorgan, Iran.
3- Associated Professor, Department of Agricultural Promotion and Training, Faculty of Agricultural Management, Gorgan University of Agricultural Sciences and Natural Resources
 
Abstract
Drought is one of the natural phenomena that causes a lot of damage to human life and natural ecosystems. In general, drought is a lack of rainfall compared to normal or what is expected, when it is longer than a season or a period of time and is insufficient to meet the needs. Drought causes damage to the agricultural sector. The vulnerability of the agricultural sector in each region depends on three factors: the degree of drought exposure, the degree of sensitivity to drought and the capacity to adapt to drought. A review of previous studies indicates the diversity of indicators and methods used to assess vulnerability, which indicates the importance of the issue. Institutions responsible for agricultural management can only manage drought properly if they have the appropriate tools to measure the vulnerability of the agricultural sector to drought. Therefore, the first step in drought studies is to identify vulnerable areas and assess the vulnerability of areas. Vulnerability measurement in geographical dimensions and measurement of indicators by main vulnerability components have received less attention. Based on this, the present study has investigated drought vulnerability in Golestan by scientific method and by combining the three mentioned components and has compared the exposure situation, sensitivity level and level of drought adaptation capacity among the cities of Golestan province. Golestan province as one of the important agricultural hubs is highly dependent on the amount of annual rainfall. Due to fluctuations in rainfall and drought in some parts of the province, there have been 4 outbreaks and as a result, 7-12 and 10 days of drought have occurred, which has caused severe damage to the livelihood of farming families. Therefore, the aim of the present study was to compare drought vulnerability among cities in Golestan province by three components (exposure, sensitivity and adaptation). First, by reviewing the sources, the effective indicators on drought vulnerability are identified separately by the three components and judged by experts (faculty members of water engineering, agriculture and plant breeding, agricultural extension and education, and agricultural economics and experts of water engineers). 55 appropriate indicators in three main dimensions of vulnerability, namely: a) exposure (14 indicators), b) sensitivity (26 indicators) and c) compatibility (17 indicators) were developed and data related to the indicators were collected. The weights of the indices were extracted by Shannon entropy model and by the TOPSIS method the combined index was compiled separately into three vulnerability components. The final result of the combined index was combined with the GIS layers of the cities of Golestan province, and the level of vulnerability of the cities was determined separately for the desired components. The results showed that in terms of exposure to Bandar-e-Gaz, Bandar-e-Turkmen and Aq Qala are in the first to third ranks, respectively, and are exposed to drought. Azadshahr, Galikesh and Bandar-e-Turkmen counties are in the first to third ranks with the highest sensitivity to drought, respectively. The cities of Gomishan, Galikesh and Maravah Tappeh are the most adapted to drought, respectively. Finally, the results of calculating the total vulnerability index showed that the cities of Marwah Tappeh and Bandar-e-Turkmen are the most vulnerable areas to drought in Golestan province. The findings of this study showed that rainy areas can be more exposed to drought at the same time than other areas and there is no direct relationship between rainfall and drought exposure. This confirms the findings of other studies such as Kramker et al. And O'Brien et al. On the other hand, the findings of this study showed that there is no direct relationship between rainfall and vulnerability to drought and the most  rainy areas of a region at the same time can be the most vulnerable to drought. This is in line with the findings of Tanzler et al. And Salvati et al. On the relationship between rainfall and drought vulnerability. Due to the fact that the rainy areas of this province are more exposed to drought than other areas and farmers in these areas have shown a higher degree of sensitivity to drought and are more vulnerable to drought than other areas, it is recommended Measures should be taken to reduce the sensitivity and increase the adaptation capacity of farmers in these areas.
 
Keywords: Drought, Vulnerability, Exposure, Sensitivity, Compatibility, Regional Leveling

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Zoning map Vulnerability of Flood Spreading areas
(Case study: Musian Flood spreading station in Ilam province)
 
 
 
Introduction
One of the flood plain hazards is a change in the pattern of surface flows due to natural factors or human activities. Changes in the stream pattern are the changes that occur due to the surface stream patterns in terms of the shape of the drains, drainage form and quantitative morphological indices of the basin. These changes ,by formation of flood, submersibility, erosion, longitudinal and transverse displacements of rivers and streams, environmental degradation, etc., have a great deal of risk and harm to residents of the land adjacent to the watersheds, including the demolition of residential buildings,  valuable agriculture lands, facilities, river structures, buildings and relation routes, etc. There are several watersheds in the Musian Plain Basin that regularly change the direction of surface streams and, while displacing large volumes of sediments of erosion-sensitive structures, degrades crops, rural dwellings, connection paths, facilities, Irrigation canals obstruction, water supply and a lot of financial and physical damage to the residents of the region. Therefore, in order to solve these problems, in 1997, the Dehloran flood spreading plan was carried out at a level of 5000 hectares from the Basin of Musian Plain. Although some of the changes in the dynamics of the region, such as stream pattern, flood control, supllying groundwater aquifers, etc., have been caused by the implementation of this plan, but the problem of the concentration of watersheds behind the embankments composed of sensitive formations ,and the release of these areas will have many financial and even physical losses. Therefore, with the implementation of this research, it is attempted to identify the domain and risks that threaten the lowlands and to identify the appropriate measures to prevent them from happening with the zoning and inspection of the vulnerable areas of the Musain Plain.
 
 
Methodology
This study was conducted in five stages to prepare a vulnerability map of the flood spreading area of ​​Mosian plain. First, the implementation phases of the flood distribution plan were separated. In the second stage, information layers of effective factors in changing the flow pattern and concentration of surface currents behind the flood spreading structures were prepared. These layers included elevation, slope, and direction classes, which were prepared based on the Digital Elevation Model (DEM) extracted from the 1: 50,000 topographic maps of the Armed Forces Geographical Organization, as well as the layers of geological formations and land use changes. The lands were prepared based on the maps of the Geological Survey of Iran and the processing of Landsat satellite images of eight OLI sensors in 2013, respectively, by the method of determining educational samples. In the third stage, each class of effective factors in changing the flow pattern (mentioned layers) was given a score based on the range of zero to 10. The basis of the scores of the classes of each factor was according to the number of classes and the average of the total classes of that factor. The fourth stage in the GIS environment was created by combining the weight layers created, the vulnerability layer of the study area (quantitative map of vulnerability areas) of the basin. Then, by analyzing the vulnerability layer (filtering), the pixels and small units were removed or merged into larger units. The last (fifth) step was to classify the quantitative layer and then extract the qualitative map of the vulnerability zoning according to the range of scores based on the five very low, low, medium, severe and very severe classes. A summary of the research steps is shown in the form of a diagram.
 
Results and Discussion
The results showed that the most important threat and danger factor is the concentration of waterways behind erosion-sensitive embankments. Also, the study area in terms of vulnerability includes three classes with medium risk, high and very high and covers 16, 62 and 22% of the area, respectively. Flood and upland Spreading areas, risk areas and lowland lands are the most vulnerable parts of the basin in terms of floods and sedimentary deposits.
 
Conclusion
Based on the results obtained by combining the information layersof the factors influencing the stream pattern change, the zoning map of vulnerable areas of the region was created in 5 classes. Except for very few and very small classes that are not present in the region, there are other cases at the basin level:
Medium class:Includes about 16% of the basin. The existing watersheds in this part are ranked 1th class, and some of them are entering the rivers of Dojraj and Chiqab in the eastern and western parts. The formations of this part are often Bakhtyari and limitedly Aghajari. The floors have a height of 100 to 400 meters and the gradient is from 0-2 percent to 20 percent.
Medium class: About 62% of the basin level. The watersheds that flow in this section are in 1to 5 class. The formations of this part are often alluvial and bakhtiari of lahbori sections. It has a height of less than 100 meters to 300 meters and a gradient of 2-0 percent to 20 percent.
very intense: it covers about 22% of the basin's surface. The existing watersheds are of of class 2 and 3. The formations of this part are often alluvial and bakhtiari of lahbori sections. They have height classes of 100 to 300 meters and the gradient is 5-2 percent and is limited to 5 to 10 percent in the slopes.
 
Keywords: Vulnerability, Aquifer, zoning, Satellite imagery, Environmental hazards, Musian

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Evaluation of hot spots changes in Tehran city and satellite based on land use and its role in urban heat hazards
Expanded abstract
Problem statement:
Urbanization and human activities affect the urban climate and clearly affect the air temperature close to the surface. In Tehran and its satellite, factors such as climatic region, season, time of day and wind regimes, topography, urban environments, population density, residents' activity, vegetation structure and urban physical form play an important role in the formation of urban heat islands. The purpose of this research is to determine the type of spatial distribution of heat islands of Tehran metropolis and satellite cities using land use and land cover. Replacing natural land cover with impervious surfaces due to urban development has negative environmental, social and economic impacts, in addition to beneficial aspects. Most of the albedo belong to the built areas and the bare land and the smallest of the Albedo belong to the aquatic areas and vegetation. In this research, the hypothesis is whether the suburbs may have higher temperatures than urban areas depending on the type of land use? In fact, it is examined the spatial distribution of the heat island of Tehran and its satellites, in which the use of land and land cover are analyzed as a factor contributing to the creation, intensification or reduction of the thermal island.
Methodology:
Extraction and preparation of imagery data through the Landsat 7 Satellite ETM + sensor over the years 2001-2015 and selection of June as the hottest month of the study area. These images were extracted from Route 164 and Row 35 of the USGS. An assessment was carried out through the accuracy of ground surface temperature data by Landsat satellite images and obtained temperatures from the weather stations in the area based on the Taylor diagram. In order to investigate the spatial structure of the cells obtained in each map, each containing surface temperature and heat island extraction, it used the methods of world spatial autocorrelation (high and low clustering, spatial correlation) and local (Cluster and Outlier analysis, hot spot analysis). The high and low clustering statistics show how the concentration of high or low values ​​in the region. In the next step, the results of analysis of Anselin Local Moran and hot spots were compared in map format. Hot spots were analyzed in all studied regions and in all 7 cities. The area of ​​hot spots was investigated over the course of 15 years and the results were presented in table and diagram form.
Land use was surveyed for every 7 county. In the last section were studied, the relationship between hot spots in each city and type and land use changes over 15 years.
Surface spatial analysis of the surface temperature of the area showed that the cells follow a cluster pattern and their trend towards clustering. Any kind of land cover and land use will create special features in a place that can be increased or decreased with a specific microclimate.
Explaining and results:
After selecting the years 2001, 2005, 2010, and 2015 as the sample and survey of the temperature of each land use in that year, it was determined that artifact, pasture, bare lands, forest, aquatic areas, agriculture and green spaces were respectively have the highest to the lowest temperature in the area. On the other hand, in the area of heat island in a region are Rabat Karim, Ray, Islamshahr, Tehran, Shahriar, Karaj and Shemiranat, respectively.
In spite of the reduction of aquatic areas and even bare lands, because of the large impact of green space or agricultural land was reduced the extent of heat islands during the statistical period, and on the contrary, the reduction of green space and agricultural land in places where even their forest areas have grown, has increased the levels of heat islands. This suggests that the dispersion and extent of green spaces has a more effective role in reducing the heat island compared with the creation of limited forest and planted surfaces in one place. Hence, in Tehran despite the significant growth of artifacts, due to the increasing growth of green space, the heat islands has been reduced compare with the Ray, Robatkarim and Islamshahr cities, which are exactly on its suburbs.
 
Keywords: Heat Island, hot spots, land use, Tehran, satellite cities.
 
 

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River channel changes, bank erosion and sedimentation are the natural processes of the alluvial rivers that destroy the agricultural land and damage to human installations around the river. In the present study, the CAESAR model was used to assess the changes of the Kaleibar Chai River in order to measure the variation of 3 km of its main channel.CAESAR is a cellular automata model for river system evolution. CAESAR  is a cellular model  that uses a regular mesh of grid cells to represent the river catchment studied. Every cell has properties of elevation, water discharge and depth, vegetation cover, depth to bedrock and grain  size.  It  is  based  upon  the  cellular  automaton  concept,  whereby  the repeated  iteration of a series of  rules on each of  these cells determines  the behaviour of the whole system. CAESAR has a set of rules for a hydrological model, hydraulic model (flow routing), fluvial erosion and deposition and slope erosion  and  deposition.  For  every  model  iteration,  cell  properties  (e.g. elevation) are updated according to the rules, and the interaction between an individual cell and its neighbours. For example, the amount of fluvial erosion in a cell may depend upon the depth of water in the cell and the slope between that cell and its neighbours.
For modeling, the input data such as topography (DEM), daily discharge (year 2012) and sediment grain size were prepared and then channel modifications were simulated. Channel changes were identified before and after the simulation by plotting profiles of each cross-sections and were analyzed sensitive to erosion and sedimentation.Six cross-sections were selected before and after simulation. Results showed that the channel geometry has changed. The width and depth and form of the channel have changed. And only the mean depth of the channels was changed in sections 1, 2, 6 and 4. The erosion was dominated in the cross- sections 1, 2, and 3 (the initial part of the main channel). Then the sedimentation was dominated in the cross- sections 4, 5 and 6.


 

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One of the causes of environmental hazards is the change in the pattern of surface water flow in floodplains following the construction of flood Spreading networks. The purpose of this study is to prepare a zoning map of vulnerable areas of the flood Spreading station of Musian plain  in Ilam province after the implementation of the aquifer project in this plain. To prepare this map, five factors influencing the change in flow pattern including elevation, slope, flow direction, geological formations, and landuse change were examined. Then, in the GIS environment, each class of the mentioned factors was given a score of zero to 10 based on the range and the corresponding weight layers were created. Then, by combining the created weight layers, the vulnerability zoning map of the area was created based on 5 classes: very low, low, medium, high and very high. The results showed that the most important threat and danger factor is the concentration of waterways behind erosion-sensitive embankments. Also, the study area in terms of vulnerability includes three classes with medium risk, high and very high and covers 16, 62 and 22% of the area, respectively. Flood and upland Spreading areas, risk areas and lowland lands are the most vulnerable parts of the basin in terms of floods and sedimentary deposits.

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Air pollution, as one of the most important environmental hazards in urban areas, is closely related to weather conditions. Today, pollution in metropolitan areas has become an important issue that requires the study and presentation of practical solutions to improve living conditions in this area. Therefore, understanding the relationship between synoptic systems and air pollutants helps a lot in how to solve environmental problems and future planning. Therefore, in this study, compression algorithms of carbon monoxide emission and transfer from domestic and foreign sources were analyzed. For this purpose, GEOS-5 / GMAO / NASA satellite images were used. The results showed that the highest amount of pollution from the seasonal point of view is related to the cold and early morning seasons and the lowest is related to the early afternoon and hot season of the year. And Khuzestan are densely populated carbon monoxide cores. Low pressures of the eastern Mediterranean play an important role in reducing pollutants in the southwest of the country and in the south of the country, under the influence of atmospheric currents from the topographic cut of Bandar Abbas, air streams polluted with carbon monoxide are able to penetrate into the interior to the southern half of Kerman. Increased by low pressure systems in Afghanistan and Pakistan. The Zagros Mountains also play an important role in preventing the entry of pollutants produced by western neighbors into Iran. In summer, Iran is polluted by carbon monoxide carriers by monsoon currents from central and southern Africa to Iran and has caused a lot of pollution

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The zoning of flood risk potential in the Ghotorchay watershed with ANP and WLC multi-criteria decision making methods
 
 
Extended Abstract
Introduction
Flood is one of the most complex and natural destructive phenomena that have many damage every year. The northwestern region of the country, due to its semi-arid and mountainous climate and thus of high rainfall variability, is one of the areas exposed to destructive floods. Flood risk zoning is an essential tool for flood risk management. Therefore, the purpose of this research was to determine the flood risk zones in the Ghotorchay watershed by using the analytical network process (ANP).
 
Methodology
In this research,, with geographic information system (GIS), satellite images, synoptic station data, analytical network process and the combination of layers, the flood potential of has been modeled in the Ghotorchay watershed. The final map of flood risk based on a combination of factors and climatic and physical elements including land use, geology, vegetation, topography, slope and land capability was prepared. The weight of each criterion was determined by ANP method and used by weighted linear composition (WLC) method for spatial modeling and incorporation of layers.
 
Results
The results of flood risk zoning showed that the Qal layers from geology, slopes of less than 3 precent, land capacity of units 5, 6 and 7, and as well as poor vegetation cover were identified as flood zones. The results obtained from the analytical network process model indicate the fact that part of the watershed is affected by the risk of flooding with the very high potential, which is mainly located in the downstream of watershed. For this reason, the streams of rank 3 and 4 are considered as flood zones and flood guide areas to the downstream areas. Also, river networks of 5 and higher ranks are in the range of floodplains or river coastal and usually have surface and extensive floods.
 
Conclusion
The flood prone areas and providing effective solutions for flood management is one of the main steps in reducing flood damage. Therefore more precise management and control of basins with multiple dams, embedding flood alert systems in flood plain areas and performing basic measures is one of the most urgent measures to prevent, improve and control this natural disaster.
Key words: Analytical network process, Biological protection, Floodplain, Flood risk assessment, Ghotorchay
 

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 Investigation of morphometric indices of Assaluyeh, Varavi and Kangan anticlines in Fars Zagros and their relationship with tectonic activity
 
Extended Abstract
Introduction
Anticlines are the most prominent surface landforms whose geometry and morphology reflect mechanism of their formation and are keys to assessing the existence of deep faults that are effective in their formation and are among the most important seismic sources.
Detachment folds are formed by buckling of the rock units in response to shortening and are typically symmetric folds. Alternatively, asymmetric folds at the surface may be forced by the propagation of thrust faults at depth (fault propagation folds) or result from thrust movements along footwall ramps in the sedimentary pile (fault-ramp folds).The Zagros folds have often been interpreted as completely detached along the Hormuz salt.
Structurally, the study area is a part of the folded and coastal Zagros whose geological structure is simple and gentle and comprises a series of near-compact anticlines with a near-vertical axial surface and a northwest-southeast trend.
Outcrops of lithological formations in the study area include Surmeh, Fahliyan, Gadvan, Dariyan, Kazhdumi, Sarvak, Ilam, Gurpi, Pabdeh, Gachsaran, Mishan, Aghajari and Bakhtiari. In the northwestern part of the Kangan anticline, uplift of salt diapir along the Darang Fault has led to the exposure of limestone, shale, dolomite and anhydrite units of the Khami Group.
Assaluyeh is one of the most important economic bases in Iran and also one of the largest energy production areas in the world. With the rapid development of Assaluyeh region and increase of residential, urban and industrial constructions and refinery facilities, without attention to environmental hazards and especially earthquakes, it seems necessary to conduct this research.
The aim of this study was to investigate the morphometric characteristics of the Assaluyeh, Veravi and Kangan anticlines and its relationship with active tectonics in the region.
Methodology
At first, topographic, drainage network, slope, slope direction and tectonic maps of the anticlines were prepared using digital elevation model data, Landsat imagery and field surveys. Then, the geomorphic quantitative indices of the fold front sinuosity, aspect ratio, fold symmetry index, fold surface symmetry index, anticline crestline index, fold elevation index and spacing ratio were calculated. Qualitative studies were carried out on drainage pattern indices, triangular facets, wineglass valleys, linear valleys, fault scarps, springs, alluvial fans, etc. Finally, the relationship between all geomorphic and tectonic parameters was analyzed.
Results and discussion
Fold symmetry index is one of the most important parameters that show the degree of inequality of the two limbs of the anticline and thus the intensity of tectonic activity. In a completely symmetric anticline, the value of this index is 1, while in an asymmetric anticline the value of this index is less than 1. The index values for all three anticlines are less than one, but the Asalouyeh anticline shows more asymmetry, indicating a high tectonic activity on the anticline.
The fold front sinuosity index indicates the degree of tectonic activity or age of the folding system. The values obtained for this index in the three anticlines indicate that the anticlines are young and the tectonic forces are dominating the erosion.
The high value of the aspect ratios indicates the elliptical shape of the anticline, which is caused by the high stress perpendicular to the axis of the anticline. The index for Varai, Kangan, and Asalouyeh Anticlines are 0.7, 0.5 and 0.5, respectively, which again indicates nearly high tectonic activity in all three anticlines.
The spacing ratio index at the northern flank of Varavi and Assalouyeh anticlines and the southern flank of  Kagan anticline indicate a high value. Quantitative index of surface symmetry of folds also shows that all three anticlines are asymmetric and the asymmetry of Asalouyeh anticline is greater than Kangan and Varavi anticlines.
The drainage pattern is another indicator that, in the absence of tectonic evidence, can be a key to identifying tectonic activity.
The existence of asymmetric fork drainage networks is evidence of active tectonic evidence indicating lateral growth of anticlines. According to this criterion, Varavi anticline has grown to the northwest.
Comparison of the valleys shows that most of the valleys in Kagan anticline are of wineglass type whereas in Asalouyeh and Kangan anticlines linear valleys are more abundant. Some of these valleys are formed along transverse faults. The presence of numerous alluvial fans in the slopes of the Varavi anticline, indicates rapid erosion of the valley bed due to the rapid uplift and increasing valley slope. The presence of elongated and narrow V-shaped valleys is another evidence of the high tectonic activity of this anticline.
Conclusion
In seismicity studies and identification of hidden or blind fault studies, geophysical and geotechnical methods are expensive, time-consuming and require special equipment and are performed on a small scale. With the availability of landforms and features, risk assessment will be done at a lower cost, faster, and on a larger scale, if a relationship between landscapes and earthquakes can be established.
The geometry of the folds reflects the mechanism of their formation. Asymmetrical folds are associated with deep faulting and a detachment horizon, where the movement of sedimentary layers on the detachment horizon or at the tip of the hidden faults can cause an earthquake. The three anticlines of Assaluyeh, Varavi and Kangan are also part of the folded Zagros and have the characteristics of the folded Zagros.
In this study we defined a new index related to fold morphology, called fold surface symmetry index. Also we used fold morphology to detect the presence of detachment horizons and faults in the core of anticlines and their relationship to seismic hazard risk.
The results of this study show the transverse profile asymmetry of all three anticlines due to the association of these anticlines with the longitudinal faults in the anticline core and along their axes. The results of measurements of aspect ratios, fold front sinusitis, anticline ridge, and study of drainage patterns and tectonic landforms such as fault scarps, triangular facets, linear valleys also confirm the high tectonic activity of all three anticlines and the potential for earthquake hazard due to the movement of deep faults or any segments of them.

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Comparison of the effectiveness of four artificial intelligence methods in predicting drought
Abstract
Problem statement:
Drought is a temporary disorder whose characteristics vary from region to region, therefore, it is not possible to define a complete and absolute definition of drought. Drought is one of the most important natural disasters that can occur in any climate regime. Since drought is unavoidable, it is important to know it in order to optimally manage water resources. Drought prediction can play an important role in managing this phenomenon. In other words, recognizing and predicting this phenomenon is one of the topics of interest for scientists who are interested in solving the problem of water shortage. More than 80% of Iran's area is covered by arid and semi-arid regions and lack of rain is a predominant phenomenon in this region. So far, several methods have been proposed to predict drought. Each method offers different results in specific conditions.  Therefore, identifying the best method for predicting drought in the climatic conditions of central Iran is essential.
 
Material and methods:
In this research, in order to introduce a suitable method for predicting drought for the next month, four methods of artificial intelligence including Deeplearning (using the Alexnet network, one of the convoluted networks), K nearest neighbor algorithm (KNN), multi-class Support vector machines (SVM-MultiClass) and decision tree have been used. Monthly rainfall data from 11 syntactic stations of Yazd province during the 29-year statistical period (1988 to 2017) were used as experimental data. Standardized precipitation index (SPI) was calculated to indicate drought status in terms of severity and duration on 1, 3, 6, 9, 12 and 24 month time scales. Precipitation data was used as neural network input and SPI classification as network output and 80 percent of the data was used for training and 20 percent for testing the networks.
In this study, the Recurrence Plot method was used to interpret the time series to convert these series into images and RG and B pages were created. To convert rainfall data into photos at 1, 3, 6, 9, 12 and 24 month time scales, photo layers were created using a standardized rainfall formula, and by merging these three output layers into colored photos and black and white photos were obtained. Using three pages created in MATLAB software and merging them, the output was in the form of a photo, which was placed as the input of the Alexnet network. Combination of Recurrence Plot to create images and deep learning network for classification of drought data has been used for the first time in this research. To evaluate the effectiveness of the classification strategy, standard criteria of accuracy, micro-F1 and macro-F1 were used.
 
Results Description and interpretation:
 The results showed that all networks were able to predict drought. However, on short time scales such as 3 and 9 months, the accuracy assessment criteria for some classes are zero and the methods of learning from these classes are practically ignored due to their lack of data. But on a larger time scale, this issue has been addressed and the data of those classes are well categorized. Deep learning network with image input could not predict well in the short term due to lack of data, but in the long term due to increased data has improved its performance and had the best performance. The SVM method at different time scales has shown unreliable and variable behaviors that can not be said to be a suitable method for predicting drought at different time scales. Decision Tree and KNN methods have been able to predict drought better in the short term than in the long term. The two methods have been closely related. .Based on the Deeplearning network macro-f1 evaluation criterion, the one-month time scale with 22.71% was the most inefficient method and the Decision Tree with 64.65% was the most efficient method, But with the increase in time scale, the Deeplearning network improved its performance, so that at the 24-month time scale with 65.35%, the best performance for the Deeplearning network followed by the SVM-MultiClass network with 57.40%. For future research, it is suggested that Decision Tree and KNN methods be used to predict short-term drought. In this study, with increasing the time scale and increasing the data used, these two methods have lost their effectiveness compared to the short term.
 
key words: Drought, Standardized Precipitation Index, Artificial Intelligence, Deep Learning, Alexent, Recarence Plot
 

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Application of Sentinel 5 satellite imagery in identifying air pollutants Hotspots in Iran
 
Shiva Gharibi¹, Kamran Shayesteh²
1- PhD Student of Environmental Science, Malayer University, Malayer, Iran.
2-Assistant professor, Department of Environmental Sciences, Faculty of Natural Resources and Environment, Malayer University, Malayer, Iran
 
k.shayesteh@malayeru.ac.ir
Extended abstract
1- Introduction
Today, poor air quality is one of the most important environmental problems in many cities around the world. Air pollution can have a devastating effect on humans, plants, organisms, and human assets, and efforts are being made to anticipate and analyze the amount of distribution and transmission of air pollutants in order to minimize the adverse effects on air quality and climate. Among the most important air pollutants are (CO), (SO2), (NO2), (O3) and aerosols (AI). Numerous studies have been conducted on the monitoring of these pollutants based on information and statistics from pollution monitoring devices, but the use of satellite images in the field of monitoring and measuring pollutants has been limited. Due to the increasing growth of these pollutants, in this study, an attempt has been made to identify the average spatial concentration of the most important air pollutants as the actual sources of pollution on the scale of Iran from October 2018 to December 2019. Also, identifying the most polluted centers in Iran based on the average of 5 pollutants is another goal of this study. Therefore, the aim of this study is to demonstrate the ability of Sentinel satellite to monitor air pollutants, and the ability of GPW images to produce a population density map for the first time on an Iranian scale.
 
2- Methodology
 Using the Python programming language in the Google Earth Engine program environment, various products related to CO, SO2, NO2, O3 and AI pollutant images, obtained from Sentinel-5 satellite images during the study period and in the scale of Iran, were obtained for monitoring of air pollutants and determination of pollutants focuses. The output variable is defined as a set of images based on the time filter (2019) and the spatial filter (Iran borders). The output of the average concentration of pollutants for each month is calculated separately and annually in these filters. Then, the spatial map of the average concentration of pollutants in the Arc map software was analyzed and statistical information related to the average concentration of these pollutants was processed by SPSS statistical software. To determine the hotspots in terms of all pollutants, the raster location map of each pollutant was classified using the Jenks algorithm. In order to identify the share of provinces and counties, the amount of pollutants was also analyzed by spatial statistics in GIS environment and using the Zonal Statistics command based on the defined administrative boundaries. The G statistic was used for Cluster analysis, and in order to identify Hot Spots and Cold Spots, Getis-Ord Gi statistic (Gi) was used in GIS environment.To determine the population of each province, the latest census information of Iran as well as satellite images related to the fourth version of Gridded Population of World (GPW) product were used. Finally, The Moran index was used to determine the pattern of pollutants distribution and the spatial autocorrelation.
 
3- Results
 Spatial output from the processing of Sentinel-5 satellite images during the study period for identifying air pollution centers in Iran showed that the highest levels of nitrogen dioxide were recorded in the majority of cities in Tehran and Alborz provinces and then in the centers of other provinces. In the case of carbon monoxide, the highest rate is in Tehran and the coasts of the Caspian Sea and Khuzestan, and coastal areas of Bushehr and Hormozgan provinces. The highest amount of ozone is in the northern parts of the provinces of West and East Azerbaijan, Ardabil, Gilan, Mazandaran, Golestan and Northern Khorasan. Most of the dust was in the southern, eastern, southeastern and central provinces of Iran. The highest amount of sulfur dioxide pollutants is recorded in Tehran and then in the provinces of Khuzestan, Kerman, Hormozgan, Bushehr, Markazi, Qom, Isfahan and Khorasan Razavi. Provincially, the highest share of nitrogen dioxide is in the provinces of Tehran, Alborz, Qazvin and Qom. The highest provincial share of carbon monoxide is in Khuzestan, Gilan and Mazandaran provinces. The highest share of dust is in the southeastern provinces, including Sistan and Baluchestan, the highest share of sulfur dioxide is in Khuzestan province, and the highest share of ozone pollution is in the coastal provinces of Caspian Sea. Compliance of the average 5 pollutants with Google Earth images showed that the contaminated areas are located in the cities of Abadan, Imam Khomeini Port, Mahshahr Port and Ahvaz (Khuzestan Province), Tehran, Pakdasht (Tehran Province) and Assaluyeh Port (Bushehr Province). The results of comparing the average concentrations of pollutants in different seasons showed that there was no significant difference between CO, NO2 and O3 pollutants in different seasons, but suspended particles and aerosols in winter and autumn seasons have a significant difference with the amount of this pollutant in spring and autumn. Also, SO2 pollutant in autumn had lower concentrations than other seasons. The results of clustering analysis to determine the status of significant spatial clusters showed that the data are in the confidence range and have spatial auto-correlation and cluster distribution pattern.
 
4- Discussion & Conclusions
 According to Sentinel-5 satellite images, most of the pollution centers in Iran are related to petrochemical industries and refineries, which are located in the cities of Abadan, Imam Khomeini port, Mahshahr port and Ahvaz (Khuzestan province), Assaluyeh port (Bushehr province) and common pollutants. By these centers are NOX, SO2, CO, suspended particles and aerosols. Also, other centers (Tehran, Pakdasht in Tehran province) are located in the most populous urban areas of, which have been identified as hotspots in high production of NO2 and CO, due to high population and urban traffic.  Due to the higher population density of Tehran and Pakdasht than other cities in Iran, air pollution can be more important in these cities. Therefore, the use of satellite imagery to monitor Iran's air pollutants and the location of hotspots can be very cost-effective and time-consuming.
 
Keywords: Air Pollution Monitoring, Sentinel, Satellite Imagery, Polluted Hotspot, Moran’s Index.
 

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Evaluation and comparison of the accuracy of fault and seismic data in fractal analysis of northwest Zagros tectonic
Introduction
Complexity of natural processes especially tectonic processes that shape landscapes cannot be evaluated by classic geometry. In comparison with integer dimension of Euclidean space, fractal geometry can analyze features with non-integer dimension (Turcotte, 1977:121). Fractal behavior in such features shows self-similarity that this component is independent of the accuracy of investigation (Baas, 2002, 311). In fact, fractal dimension, is scale-invariant (Phillips, 2002, 144). Spatial variations of fractal parameters are an important factor in studying the tectonic state of regions. By determining the fractal dimension of Linear structures such as faults, it is possible to compare their geometry disorder (Suk moon et al, 1996:5). This parameter affects seismic behavior of fault because earthquake is an event related to faulting (Bachmanov, et al, 2012: 221) and Their concentration in an area indicates tectonic activity. In this research we performed fractal analysis using box counting method on fault and seismic data of northwest of Zagros about different scales of fault and different time periods of earthquake epicenters of two organizations with various detail to find and examine their fractal behavior by fractal dimension.
Methods
Data in this research can be divided to three clusters: 1. Fault lines of two scales of geology maps (1:100000 and 1:250000), 2. Earthquake epicenters of two periods of times prepared by two organizations (20 century data of Institute of Geophysics and 1900-2020 data of International Institute of Earthquake Engineering and Seismology) and 3. The second cluster with exert of Magnitude of completeness of earthquakes that show the minimum magnitude above which the data in the earthquake catalog is complete. Fractal analysis applied on these data by box counting method. To achieve this goal firstly, under study area divided to 6 boxes that contain main fault trends horizontally and vertically (A: folded Zagros in west of Kermanshah, B: faulted Zagros around Kermansha and east of kermansha, C: folded Zagros near mountain front fault, D: An area between faulted and folded Zagros near Khoramabad, E: Area around Balarud fault and F: An area between Balarud and mountain front fault to faulted Zagros). To calculate fractal dimension of fault lines and distribution of earthquake epicenters, box counting method suggested by Turcotte (1997) were applied by using Hausdorff dimension, which in two quantity of size (side length of grids) and number (number of grid boxes containing earthquake epicenter or fault) are used to calculate FD (total fractal dimension) value (Schuller et al, 2001: 3). Relation between reciprocal of side length (quantity of size) and number of boxes containing point and linear features (quantity of Number) was drawn Logarithmically as a linear regression in Excel that shows fractal dimension.
Result and discussion
Larger values of fractal dimension indicate greater geometric disorder (Sukmono et al., 1996: 5). Analysis of faults of two scales represent that faults geometry is fractal and the amount of FD for scale of 1:100000 compared with scale of 1:250,000 is larger but their result approximately is same. The FD values for both scales are locate between 1 and 2 that expresses development of the fractal community of faults has a linear trend. On the other hand, for earthquakes, increase in FD values shows that earthquakes are not clustered and are distributed homogeneously (Oncel & Wilson, 2002: 339) along a line in understudy area. Calculated number-size values for faults and earthquakes represent both partial and popular FD changes. Based on partial FD, two populations can be classified: (a) Background with FD larger than popular FD; (b) Threshold with FD lower than popular FD.
Conclusion
Fractal analysis of faults of two scales of geology maps reveals that the order of active areas with high FD values in both scales are same but due to different details of faults in geology maps of geology survey and oil company, in scale of 1:100000 area labeled B and in scales of 1:250000 area labeled A is the most tectonically active region, however, area labeled E in both scales has lowest value. The order of active areas based on FD values for earthquake epicenters of 1900-2021 data of geophysics institute do not support other results because area labeled C with low density of faults and earthquake epicenters is in the first order and area labeled A is on the contrary of it. However, FD results of 20 century earthquake epicenters with exert of magnitude of completeness are reliable and higher magnitude of earthquakes spatially recent Ezgeleh earthquake in area labeled A is its evidence.
Keywords: Fractal, Tectonic, Northwest Zagros, Fault, Earthquake
 

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

• : Climate Change, Climate Scenarios, Uncertainty, LARS-WG, Seymareh.

 
 

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Prioritization analysis of effective factors in non-participation of local societies in desertification projects (Case Study: Ain Khosh region, Ilam province)
 
Introduction
Participation in social affairs is a commitment and acceptance of individual and social responsibility that all human beings will have to accept. This commitment and responsibilities may take the form of definite and unlimited activities. By increasing the population and the complexity of the goals and efforts of the human community to advance economic, cultural, social and political goals, we inevitably need partnership and cooperation. Participation means using personal resources to participate in a collective action. The first step is to increase popular participation in desertification initiatives, identify and remove barriers to effective non-participation in project implementation. The main objective of this research is to prioritize the factors affecting the lack of public participation in desertification plans using the FUZZY-AHP method and the Friedman nonparametric test.
 
Materials and methods
The statistical population of the study consisted of households in Ein-e-Khosh village of Dehloran Ilam and experts of Ilam University and natural resources organization of Ilam province and Dehloran County with more than ten years’ experience in combating desertification issues. In this research, the indexes and sub-indicators related to library studies, questionaire from experts of the university, experts from the Natural Resources Department of Ilam province and Dehloran city, as well as referring to the region and interviews with the residents of the region were identified. Then, the questionnaire designed by the FAHP method evaluated by inconsistency rate and its validity and reliability by Likert scale, and finally tried to prioritizing them based on the following steps. First, the prioritization of the indicators was performed from the expert's point of view using Fuzzy Analytical Hierarchy Process (FAHP). Then, the prioritization of the indicators from the perspective of experts by application of Friedman test and finally, the priority of indices and sub-indicators by the local point of view with Friedman's test.
 
Results and discussion
The findings of the research showed that the ranking of indices using Friedman's nonparametric test is based on the average rating from the viewpoint of residents of the region, respectively, economic index, design-executive, educational-promotional, and social. Also, this prioritization from the perspective of experts using the FUZZY-AHP test is design-executive, economic, educational, promotional and social priority, respectively. Also, the results showed that in the total of 15 identified subcategories influencing the lack of public participation in combating desertification projects, from the perspective of experts, the sub-design of the design-executive entitled "Short-term, mid-term and long-term non-planning for participation" with the average rating of 11.68 was the most important and the sub-index, "Migration of youth in the countryside" with the average rating of 3.59, is the most insignificant sub-indicator. However, from the perspective of residents in the region, the underlying economic indexes "disregarding people's income as a direct incentive to implement combating desertification projects" with an average of 11.24, the most significant and sub-indicator of the design-executive "lack of full allocation of funds during the implementation of combating desertification projects" with average rating of 5.63 is the most significant sub-indicator, which indicates that economic indicators and design-executive, along with the sub-indexes, are the most important reasons for non-participation of people in combating desertification projects in the study area.
Due to the fact that the indicators and sub-indicators are identified based on the opinions of experts and locals in the study area, this has led to familiarizing the respondents with the research. In this research, the FAHP and Friedman test were used. According to the topic of the research in the field of public participation, the best tool for measuring the comprehensive statistical view of experts including experts and locals with regard to the study area is considered. In the reliability of the FAHP questionnaire, the responsiveness questionnaire has high reliability with regard to the multi-stage and multi-stage couples comparing method and the incompatibility rate test (mean 0.043 inconsistency rate). Cronbach's alpha test was used for Likert scale questionnaires. Results (Cronbach's alpha = 0.83) showed that the questionnaire had acceptable reliability. So, results is in consistence with other researchers' findings, including Saleh Pourjem et al. (2017).
 
Conclusion
According to the results obtained from prioritization, it has been shown that in the subject of participation, in spite of the difference between the views of experts and the people of the region, in some cases, the main priorities in the discussion of non-participation are almost similar; these results are consistent with the studies of previous researchers such as Saleh Pourjem et al. (2017).
It is suggested that the removal of obstacles to public participation in combating desertification projects be put on the agenda of trusteeship organizations and public participation in all stages of design, implementation and future protection in the combating desertification projects will be considered.
 
Keywords: People’s Participation, MCDM, FUZZY-AHP, Friedman Test
 

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Risk modeling of plant species diversity and extinction in Sorkheh_hesar National Park
 
Zahra Mosaffaei¹, Ali Jahani^2*, ³MohammadAli ZareChahouki, ⁴Hamid GoshtasbMeygoni, ⁵Vahid Etemad
 
¹ Masters of Natural Resources Engineering, Environmental Sciences, College of Environment, Karaj
^*2Associate Professor, Department of Natural Environment and Biodiversity, College of Environment, Karaj.
³ Professor, Department of Restoration of arid and mountainous regions, University of Tehran, Karaj
⁴ Associate Professor, Department of Natural Environment and Biodiversity, College of Environment, Karaj
⁵ Associate Professor, Department of Forestry and Forest Economics, University of Tehran, Karaj
 
 
Abstract
Full identification of hazards and prioritizing them for non-harm to nature is one of the first steps in natural resource management. Therefore, introducing a comprehensive system of evaluation, understanding, and evaluation is essential for controlling hazards. This study aimed to model and predict environmental hazards following increased degradation in natural environments by ANN. Thus, 600 soil and vegetation samples were collected from inhomogeneous ecological units. Soil samples were prepared by strip transect method according to soil depth in four profiles (5, 10, 15, 20 cm). Vegetation samples were also collected using a minimum level method using 2 2 square plots according to the type, density, and distribution of vegetation. Sampling was done in two safe zones and other uses were modeled using ANN in MATLAB environment. The optimal model of multilayer perceptron with two hidden layers, sigmoid tangent function and 19 neurons per layer and coefficient of determination of 0.90. The results of sensitivity analysis showed that soil moisture content would be effective in decreasing biodiversity and flood risk as well as increasing the risk of extinction of endemic species in the region, and then the apparent and true gravity and soil porosity and distance from the road play a key role in the degradation of cover. Vegetation has increased flooding and extinction risk. Therefore, it is recommended that measures related to soil and vegetation restoration in this park be taken to reduce future damages as soon as possible.
 
Keywords: Modeling, Artificial Neural Network, Environmental Hazards, National Park, Vegetation
 

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Despite extensive studies on the relationship between land subsidence and groundwater level, less research were focused on the impacts of distance to pumping wells on variation of land subsidence area. This study presented the linkage between the ground surface displacement rate and groundwater pumping area and the associated geomorphic consequences. The land subsidence rate was extracted from Sentinel-1A images. Then, to evaluate the relationship between the ground surface displacement extent and distance from the pumping wells, 30 pumping wells were identified within the study area. Different buffers at specified distances (500, 700, 1,000, 1,300, 1,500, 2,000 m) were created around each well. To test the effect of the distance to the pumping wells on the spatial extent of critical and slight subsidence areas, average annual images of land subsidence were classified into two classes, including areas with a maximum subsidence rate and a minimum subsidence rate. Further, earth fissure identified by GPS were transformed to the land subsidence classification map. The results showed that there is a significant relationship between the distances to pumping wells and displacement extent. The spatial extent of areas with the maximum subsidence rates decreased as the distance from the pumping wells increased. By contrast, the spatial extent of areas occupied by the minimum subsidence rates increased with increasing the distance from the pumping wells. Also, the density distribution of the earth fissures increased in areas with the maximum subsidence rate.