Journal of Spatial Analysis

---

 Relationship between hydrogeomorphic features and suspended sediment load under Kashfarud basins
 
Introduction
As a stressful stimulus, river sediment is the most significant threat to aquatic ecosystems. To prevent or minimize the damage, three stages of the erosion process should be investigated (Naseri et al., 2019: 83). Determining the amount of sediment transported by rivers is important from different aspects. Sediment carried by water flows is considered a factor effective in shaping the geometric structure and geomorphic characteristics of rivers (Tashekabood et al., 2019: 282).
Data and methodology
To estimate the amount of annual suspended sediments, the flow and sediment statistics of hydrometric stations (8 stations) and meteorological stations (13 stations) were employed (Figure 2). The research statistical period is 25 years (1993-2017). The altitude, area, and perimeter of the basins were obtained from topographic maps with a scale of 1.25000. To investigate the correlation between independent and dependent variables, the normality tests of Shapiro-Wilk and Kolmogorov-Smirnov were performed in SPSS16 software. To extract the geomorphic features of the basins, the digital elevation model was used. Then, ground surface corrections and pretreatments such as removal of hydrological pits were performed and ground drainage pattern was determined.
Stepwise multivariate regression
In the present study, stepwise multivariate regression was used to reduce the number of independent variables and determine the effective factors in the sedimentation of the basin. This method investigates the effect of several independent variables on a dependent variable (Zare Chahuki: 2010). In stepwise multivariate regression, the independent variable that has no more significant effect on the dependent variable is removed from the analysis, hence excluded from the equation. The general form of the stepwise regression equation is:
Equation 1                                                                            Y= a + B1X1 + B2X2 + …… + BnXn + e
Data description and interpretation
The principal component analysis method was used to determine the most effective characteristics of sediments as well as their grouping. In principal component analysis, variables that have a high correlation and are distributed in a multidimensional space are reduced to a set of non-correlated components, each of which is a linear combination of the main variables. The obtained non-correlated components are called principal components (PCs). Prior to component analysis, the KMO coefficient was used to ensure the appropriateness of the data for principal component analysis. This coefficient fluctuates in the range of zero and one and if its value is less than 0.5, the data will not be suitable for principal component analysis and if the values of this coefficient are between 0.5-0.69, The proportionality of the data is moderate and if the value of this coefficient is more than 0.7, the data will be quite suitable for performing principal component analysis.
Regression analysis results
In this study, the sediment weight of the basin was considered as a dependent variable and other parameters as independent variables. The variables of slope, precipitation, basin length, Elongation Ratio (R), circularity coefficient, and unevenness of the basin have a higher correlation with the amount of sediment production in the basin than other variables.
An eigenvalue was used to determine the number of factors. The minimum eigenvalue for the selection of final factors is 1, and factors with an eigenvalue bigger than 1 are considered final factors. The results showed that the three factors of circularity coefficient, compactness coefficient, and basin form coefficient have an eigenvalue bigger than 1.
Conclusion
The results showed that geomorphic parameters have a high correlation with the amount of annual sediment. The results showed that seven factors of slope, precipitation, basin length, elongation ratio, circularity coefficient, unevenness coefficient, and form ratio of the basin were the most important in estimating the amount of suspended sediment based on the principal components analysis method. The average of special sediment varies from 134 tons per year in Dehbar basin to 16 tons per year in Kardeh basin and also the average annual sediment varies from 261.6 tons per year in Golmakan basin to 156.7 tons per year in Shandiz basin. Evaluation of Bartlett's test of sphericity tests and KMO values is 0.9. Therefore, the data is suitable for factor analysis. The percentage of variance explained by each factor indicates that the circularity coefficient with 50.71% of the variance explains all the research variables. In total, three factors of circularity coefficient, compactness coefficient, and form ratio of the basin could explain 82.6% of the variance of all research variables. Therefore, the results are consistent with Lu et al. (1991), Sarangi et al. (2005), Tamene et al. (2006), Zhang et al. (2015), Salim (2014), and Ares et al. (2016).
Khorram Dareh sub-basin with heavy rainfall (504 mm) has the lowest specific sediment, which is due to the geological structure of the region. Based on the calculated indicators, most of the studied sub-basins are elongated. The form ratio of the basin is less indicative of the elongation of the basin. The highest branching ratio of the basins is in the vicinity of faults. Also, high circularity values indicate points prone to sedimentation. River sections up to degree 3 are located in more subdued areas and have a steeper slope. Golmakan, Khorram Darreh, Zashk, and Dehbar sub-basins have a high potential for sedimentation. Regression equations of sediment measurement curves are usually used in sediment load estimates. The most important reason is the ease of application of these equations. According to the research results, it can be concluded that the integrated use of principal component analysis, cluster analysis, and multivariate stepwise regression has a suitable and acceptable efficiency in estimating suspended sediments. Testing the regression model concerning different climatic and hydrological regimes of Iran’s watersheds to achieve an efficient pattern of using these equations can be fruitful in estimating sediment load in different regions.
 
Keywords: Hydrogeomorphic, Sediment erosion, Kashfarud basin, Stepwise multivariate regression

---

Explanation of the subject: The annual drought phenomenon, by affecting economic, social and environmental issues, leads to the vulnerability of urban and rural households and the instability of their livelihoods. Yazd is one of the provinces with drought. Consecutive droughts in the province necessitate integrated management and community adaptation in times of drought.
Method: Taking into account the length of the statistical period of 20 years and to obtain the results with a high level of confidence, the main data of the census documents that have been compiled for the development of cities and villages have been used. By analyzing the main components of several factors, it was selected as the main components. By calculating the standard precipitation index in the arid region, the driest year was determined and by calculating the weighted average of their correlation index with the main components of socio-economic and ecological environment based on appropriate statistical inference. At the end of the year, the effect of drought on different dimensions was presented by step-by-step linear regression, analysis and communication between them to adapt and resilience of individuals in society.
 According to the general results, one of the most important economic and dry economic losses is the annual income of the villagers, which can be due to the decrease in the area under cultivation and production of the main agricultural products. In the social sector, people with knowledge and awareness should increase their adaptive capacity to the occurrence of drought, in order to reduce the vulnerability of social issues to the phenomenon of drought. The results show that unemployment insurance has increased following the drought. The main reason for this is the unemployment of farmers affected by drought, so changing jobs along with temporary migration or the production of handicrafts, etc. can increase the relative income of households at the time of occurrence and prevent unemployment in these conditions. Increasing unemployment will cause other social harms such as poverty, declining health, increasing disease, and reducing judicial and social security. According to the results, one of the components that has established a high standard of rainfall during the drought year is the theft of livestock, which shows a decrease in the social security of the community. People in the study community increase their adaptability to the annual drought by increasing breeding work, such as rangeland improvement, rainfall collection, biological improvement, afforestation, and irrigation reform.

---

Spatial analysis of natural resilience in border areas
Case study: Zahak county
problem statement
Occurrence of natural disasters such as drought, floods and earthquakes in geographical areas, especially in rural areas, often have devastating effects. Hence, resilience has become doubly important, especially in special areas that are of special importance and sensitivity. On the other hand, border areas have a special place in policy-making and planning is important in this areas. One of these areas is Zahak county in Sistan and Balochestan province, which due to the instability and reduction of the inflow of river water resources, as well as climatic conditions and drought in combination with other factors, the traditional employment opportunities of the often rural population face serious challenges and therefore the county5b is deprived. Increasingly, the sustainability of livelihoods is facing problems. The question is, how do the spatial zones and the villages located in these zones react to the change of internal and external natural factors? Which areas and villages are more resilient?
 
Method of research
This article deals with the spatial analysis of environmental resilience in Zahak county and its purpose is to investigate the differences in resilience in different areas of the county. The general approach to the study is integrated and descriptive-analytical in terms of method. Data were collected using documentary and field methods with observation tools and questionnaires and findings of a specialized panel. The statistical population of this research is the villages of more than 20 households in Zahak city that have had governor of a rural district or village council.
.
Description and interpretation of results
The villages of Zahak county are threatened by the threat of these resources due to their dependence on natural resources. The results show that none of the defined geographical areas in the rural area is sustainable and three rural areas are semi-sustainable and one rural is unstable. Assessment of sustainability in micro zones also shows that naturally unstable villages are often sparsely populated, which means that activity has not developed either. After qualitative and quantitative evaluation of various natural and environmental indicators in the county and their impact on the resilience of places and settlements in the county, settlements and places in terms of resilience were classified into three levels of high, medium and low resilience. In total, 46.7% of settlements and places are at high level of resilience, 37% at medium level and 16.3% at low level of resilience. After matching the settlements and places with the geographical area of ​​the village, three of the four geographical areas are in transition and one is unstable. This study shows that the resilience of individual villages cannot perform well when it is located in areas surrounded by villages with low resilience and the whole area becomes unstable. Thus, in special areas such as Zahak county, crisis management should focus on providing natural resources and preventing vulnerability to natural crises, and it is expected that with natural stability, housing and activity will be sustainable.
 
Key word
Resilience, special areas, Zahak county, border areas, geographic zoning
 

---

Evaluation of SADFAT model performance in daily forecast of Land Surface Temperature in the city of Tehran
 
Abstract
The high spatial and temporal limitations of TIR images for use in urban climatology have been identified as a current scientific challenge. Therefore, the use of Data Fusion Algorithms in Remote Sensing has been considered. In the old methods, two bands of one sensor were used for Data Fusion. In these methods, a panchromatic band was used to increase spatial accuracy, so only spatial resolution was increased. To solve this problem, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was used to integrate the images of two Landsat and Modis gauges to increase the spatial and temporal resolution of the reflection. but, this algorithm is designed for pixels and unmixing areas that are the same in Modis and Landsat pixels. The use of this model was not suitable for urban areas with a different of landuse. Therefore, the Enhanced STARFM model (ESTARFM) was developed. The ESTARFM model was improved in 2014 to predict thermal radiation and LST, taking into account the annual temperature cycle and the unevenness of the earth's surface, and the SADFAT model was introduced.
In this study, the performance of SADFAT model in the use of OLI spatial resolution and MODIS temporal resolution in LST forecast in urban areas was examined. The metropolis of Tehran has different surface covers and multiple microclimates. So if the algorithm works successfully, This model can be used in other cities to improve urban heat island studies. The inputs for the algorithm are thermal radiance of Modis and Landsat   images, the red and near infrared band of Landsat for daily production of LST in 2017 in the city of Tehran. The algorithm uses two pairs of Modis and Landsat images at the same time and sets of Modis images at the time of prediction and then calculate the conversion coefficient for relating the thermal radiance change of a mixed pixel at the coarse resolution to that of a fine resolution. In this way, LST is generated in areas with a variety of landuse.
All the estimated pixels were compared to the base image pixels in that range to evaluate the results of the model. The comparison results for the autumn days with the average correlation coefficient of 0.86 and RMSE equal to 0.122, showed that the model has the highest accuracy in this season and in other seasons with the average correlation coefficient of 0.76 and RMSE about 0.4, has provided good accuracy.
Visual interpretation of the results of SADFAT showed that this model is able to accurately predict the LST of the land cover in different surface coatings and even in areas where one or more urban land uses are mixed in one MODIS pixel.
However, the borders are well separated and the features are not combined. Although the boundaries are clearly defined, in some land uses, the predicted LST is somewhat higher than the observational image.
Landsat and Modis satellites pass through an area with a small time difference, so they are suitable for combining with each other. But in predicting reflectance with the SADFAT algorithm, there are systematic and variable errors that we need to be aware of in order to increase the output accuracy. One of the systematic and unavoidable errors is the instability of the Terra and Aqua satellites passing through at any point, ie at each satellite pass, the location of the study area in Swath and the size of the pixel changes. Due to the distance of the study area from the vertical center of measurement on the ground (Nadir), the amount of this error varies on different days and should be checked for each day. The preventable error is the sudden change in one or more images used (16 days of the same pass time interval for Landsat) is high for estimating surface reflectance with spatial and temporal resolution. These changes may be due to human factors such as air pollution or natural factors. Natural factors such as clouds and dust storms are the main sources of error in using the SADFAT model because they are sudden and temporary and cover a wide area. The occurrence of these two factors has a great impact on reflectance. Therefore, a sudden change in these factors, in one or more images, causes a large error in the calculations.
The study also found minor spatial errors in the prediction, so that even on days when the results were better, points were observed where the values ​​in the predicted LST images did not match exactly with the OLI sensor. The reason for this may be due to changes in vegetation. Although there are some systematic and variable errors in the images and the implementation of the algorithm The results of this study showed that the performance of this model is reliable for predicting the daily LST with a spatial resolution of 30 meters in Tehran.
This method is able to support urban planning activities related to climate change in cities, so it is recommended that its performance be examined separately for different land cover in the city and the efficiency of this algorithm be evaluated with other sensors such as Copernicus Sentinels.
 
Key words: Spatial and Temporal Data Fusion, SADFAT, Heat island, LST, Urban climatology
 .

---

 Explain the Processes of Modernization on the Spatial Mismatch in Urban Neighborhoods
(The case of, Region 4 of Tehran Municipality)
 
Modernization processes and modern urban planning policies have had significant effects and consequences on the spatial transforms of cities in the world and Iran. Among that processes, we can mention the growing gap between social groups and urban spaces based on a number of contexts and mechanisms that, from the late 1960s onwards, have been conceptualized and measured experimentally under what is called the “spatial mismatch hypothesis”. The basic methodology for estimating the state of spatial mismatch in cities or urban regions is based on the logic of “spatial segregation” between social groups and land uses simultaneously; Because based on the spatial mismatch hypothesis, it is not possible to explain the segregation mechanisms between social groups in the city without considering its relation with segregation mechanisms in urban spaces or land uses, and vice versa. Based on such methodological logic, the present paper has assessed the state of spatial (mis)match in Region 4 of Tehran Municipality. The method of data collection was in the form of libraries and data available in the Statistics Center (General Census of Population and Housing in 2016 and at the level of demographic blocks of the region), Road and Urban Development Organization, Municipality of Region 4. Variables used to analyze the spatial mismatch in the region
The level of education, employment in study abroad and inside the country, employment and unemployment status, level of housing infrastructure, type of housing ownership, changes in land use pattern and the amount of daily commutes in the study area.
 Findings obtained based on the defined variables and techniques used in Segragation Analyzer and ArcGIS software show that the state of spatial mismatch in this urban region (like many other cases in cities around the world) is high, but its intensity is higher in terms of job and literacy of social groups in relation to the state of activity and residential land uses. Relying on such findings, some strategies and policies have been proposed to reduce the state of spatial mismatch in Region 4, and to contribute to a more even and equitable distribution of development in this region and hence reduce poverty among the lower classes.
 
Keyword:
Urban modernization, spatial mismatch hypothesis, socio-spatial segregation, Region 4 of Tehran Municipality
 

---

 

---

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.
 
 

---

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.


 

---

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

---

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
 

---

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
 

---

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
 

---

---

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

---

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

---

 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.

---

 
 

---

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
 

---

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
 

---