جستجوی مقالات مرتبط با کلیدواژه « zoning » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Iran is a country that has so far registered 13 Biosphere Reserves (BRs) under UNESCO's Man and Biosphere Program (MaB). This program states that although countries have independence and flexibility in the zoning and management of biosphere reserves; however, they should achieve the triple goals of conservation, sustainable economic development and Knowledge production and education through triple zoning and the implementation of appropriate management plans. This article, citing reference sources and world experiences, has tried to identify and introduce the criteria and spatial indicators of core zoning, as one of the three main zones of BR, with the help of decision-making algorithm and mathematical linear relationships, provide a scientific and generalizable model for core zoning in all biosphere reserves of the country. This research identified 5 mandatory criteria and 6 preferred criteria based on similar researches and the Delphi method for the zoning of the core of BRs in the country, by observing the percentage of the core zone from BR in the range of 5 to 20%, the establishment of the core in representative ecosystems of the country (with Covering at least 10%), meeting the needs of the protection zone of the areas with the national title (at least 20%), zoning the core of the Dana Biosphere Reserve and to avoid arbitrary procedures, in actions similar to the decision-making algorithm and The linear relationships of the integration of the indices of each criterion and the criteria of the core zone have been presented. The core area of the Dena biosphere reserve was identified as 6 permitted spots in clusters using the 5 mandatory criteria of representativeness, virginity and naturalness, conservation species, habitat importance, sensitivity and biological vulnerability of the habitat, and the preferred criterion of integrity. The size of these spots is 26928 hectares, covering about 11% of the BR, the core spots covered 10-13% of representative ecosystems, about 26% of Dana National Park, and 20% of Western and Eastern Dana Protected Areas.

Urban development and increasing economic activities lead to the disintegration of the integrity of protected areas and threaten their biodiversity. this study was conducted in Angoran protected area of Zanjan province to identify the spatial pattern of threats to biodiversity and the impact of development on the integrity of protected areas. In this regard, evaluation criteria of biodiversity threats were defined in four components, which are structural, non-biological, combined threats and the risk of units being isolated. In the structural dimension, the fractal coefficient, shape coefficient and the average slope were considered. In the combined dimension, the habitat was evaluated using the artificial neural network method. In terms of the risk of units being isolated, the nearest neighbor index was used. After preparing the mentioned maps, weighting of criteria was done. In final, biodiversity threats evaluation was done by three methods weighted linear combination, ordered weighted average and Boolean. The results showed that according to the weighted linear combination method 15995 hectares of the studied area are in the range of very high risk. This value was for the ordered weighted average method 11634 hectares and for the Boolean method 17347 hectares. In general, the Boolean method has estimated the amount of high and very high-risk areas more than the other two methods.

Floods are one of the most destructive natural disasters that cause severe injuries and loss of life, major infrastructure damage, significant economic losses, and social unrest worldwide. Due to the fact that flood is a dynamic and multidimensional phenomenon, Geographic Information System (GIS) and Remote Sensing (RS) data are used to a large extent to discover the extent of flooded areas and play a special role in preparing flood risk and susceptibility maps. Flood susceptibility mapping is essential for characterizing flood risk areas and planning flood control schemes.

In this research, the identification of flooded areas in the Karun Watershed based on the Analytical Hierarchy Process (AHP) in the GIS environment and its validation with the NDWI blue index extracted from Landsat 8 satellite images has been considered. For this purpose, first, 15 effective parameters in floods occurrence including slope, aspect, elevation, curvature, rainfall, distance from stream, stream density, distance from fault, fault density, distance from road, road density, lithology, Curve Number (CN), land use, Topographic Wetness Index (TWI) and Stream Power Index (SPI) were selected and the weighting of these parameters was done based on AHP method in the Expert Choice software environment. Finally, by using the command to combine the layers based on the weighting of the AHP method in GIS, the final flood risk zoning map was obtained. NDWI water index was used to validate the flood risk map obtained.

The results of the AHP model showed that the most effective factors in the occurrence of flood risk in the Karun Watershed include rainfall, the amount of slope and the height classes, which should be considered in order to reduce flood damage and provide management solutions for these factors. Also, the results show that the downstream areas of the watershed have the highest risk of flooding and more than half of the watershed's surface (52.24%) has a medium flood potential.

Preparing a map of flood-prone areas is one of the most constructive methods that enable the reduction of flood risk damages and help planners, stakeholders and decision-makers to properly monitor flood-prone areas and ensure appropriate and sustainable socio-economic development.

Flood, like other hydrological phenomena, is an uncertain phenomenon that can occur at any time and place and is influenced by various climatic factors, physical characteristics of the basin, vegetation status and land use, and human intervention. Determining the contribution of each parameter to the flood incidence is important. At present, with the development of GIS, remote sensing (RS), and machine learning (ML) methods, very accurate modeling of flood probability can be performed. However, the construction of these models requires accurate and principled knowledge of the flood occurrence process, the study of effective parameters in flood formation, understanding of how each parameter affects flood generation, and the selection and development of appropriate models and their evaluation. Due to the importance of determining flood-prone areas in different areas, especially basins located in arid and semi-arid areas such as the study area, the present study was conducted to assess flood risk using vector mining data mining models. Random support, grass, and forest are targeted in this area.

   In the present study, to support the risk of flooding, data support models of support vector machine, field, and random forest have been used. In general, the purpose of presenting data mining models is to achieve a reasonable and accurate estimate of spatial prediction of flood occurrence, compare the efficiency of the models and select the most appropriate method for preparing a flood sensitivity assessment map. In this study, from various information such as the topographic map of scale 1: 50000 to extract level lines, a geological map of scale 1: 100000, a soil map prepared by the General Department of Natural Resources and Watershed Management of Khorasan Razavi province, digital elevation model (DEM) image with Spatial resolution of 12.5 m, Google Earth satellite imagery, meteorological data, rain gauge, statistical period of 20 years (98-78), Andarkh stations, Olang Asadi, Kardeh Dam, Marshak, Bulgur, Bala Gosh, Al, Chenaran, Moghan, Chekneh Olya, Abqad Frizi, Talgur, Qadirabad, and Kabkan have been used. Elevation, slope, slope direction, drainage networks, main waterways, and convexity of the ground surface were extracted from the DEM image and level lines. Land use of the region was prepared from Google Earth satellite images related to 2020 and in a supervised classification method. The vegetation map of the region was also prepared based on the NDVI index and from satellite images of Landsat 8 in 2018.

The elevation factor plays a key role in controlling the direction of flood movement and water surface depth. At an altitude of 2000 m and more, with increasing altitude, the flood potential in the study area increases. According to the results, among the uses of the studied basin, irrigated and garden lands produce less runoff due to more infiltration and are less prone to flooding. In the study area, at a slope of 60 degrees, due to the increase in slope, the latency of the basin is low, the amount of water infiltration into the soil is low, and as a result, the volume of floods and surface runoff will increase. Class 0/0074-0/0120 has the greatest impact on the occurrence of basin floods. The northern, northwestern, and western slopes have the potential for flooding due to heavy rainfall, long-term snow retention, and moisture. In the study area, more than 250 mm of rainfall has the greatest impact on the occurrence of floods. In the study area, due to the relatively low permeability of the soil, the soil produces more runoff and floods. Based on the results of topographic moisture index classes in the study area, classes 268/38-359/99 had a great impact on the occurrence of floods. In the study area, concave areas have a great impact on floods because the most important and effective factors in the occurrence of floods are the slope and curvature of the earth. At present, the predictability of flood sensitivity in the study area was investigated using the area under the curve. The results of this study show that in the linear support vector machine model with the best scenario M3 with the highest correlation coefficient of 0.972 and the lowest value of MAE = 0.538, and in the random forest model the best scenario M10 with the highest correlation coefficient 961 0.0 and the lowest error value MAE = 0.685, in the Chaid decision tree model, the best scenario was M8 with the highest correlation coefficient of 0.954 and the lowest error value was MAE = 0.723.

In general, according to the results of the present study, the floors with low and medium flooding potential are more located in the eastern and southern parts of the basin, so in the eastern part of the basin due to low slope and good permeability flood risk, It is average. According to the results, due to the existence of poor rangeland land uses in the western and northwestern half of the basin, the highest flood potential has been observed. The results also showed that the northern and western parts of the basin, which in terms of geology and lithology have surface formations such as marl, clay, and silt, and their permeability coefficient is very low and vegetation is low, have a high potential for occurrence. They have flooded. In this study, the models were evaluated using correlation coefficient (R) and mean absolute error values ​​(MAE). Examination of the results of the models showed that the support vector machine, chad, and random forest models with scenarios M3, M8, and M10 with the highest correlation and the lowest mean error, respectively, have high accuracy in estimating the risk of floods in the study area. In addition, the area under the curve (ROC) was used to evaluate the proposed models. Accordingly, these values ​​have more accurate results in both educational data and training data in the algorithm (SVM) and the new random forest algorithm model. This result indicates that both models have been validated in terms of modeling accuracy and validity.

The aim of this study was to determine the areas sensitive to gully erosion and prioritize the factors affecting it in Kermanshah province, using the maximum entropy method (MaxEnt model). 9 factors affecting gully erosion potential in the area Including Elevation, Slope, Slope Direction, Lithology, Land Use, Topographic Moisture Index, Surface Curvature, Drainage Density and RSP was reviewed. Of the total gullies studied in the study, 70 percent were randomly classified as test data and 30 percent as validation data. To determine the most important parameters, the Jackknife diagram and the relative performance detection curve ROC were used to predict the model. The results showed that there is no alignment between the factors used. According to the Jackknife diagram, the lithological layers, slope, height and  drainage density were the most important factors influencing the susceptibility of gully erosion, respectively. ROC curve showed 91percent accuracy of the maximum entropy method in the training phase and 72 percent in the validation phase. According to the moat susceptibility zoning map, about 23.39 percent of the area has low sensitivity, 48.14 percent very low, 9.31percent high and 4.71percent very high and 14.46 percent has moderate sensitivity.

Landslide is one of the most destructive types of erosion of slopes, causing substantial financial losses.  Identification of causative factors in the landslide occurrence and providing a zoning map of the susceptible areas is one of the basic tools for minimizing the possible damages. In this research, the landslide susceptibility map in the Neyshabur Watershed was prepared using three algorithm including, Support Vector Machine, Maximum Entropy, and Random Forest Algorithm. A map of landslide distribution was prepared along with 12 thematic layers including slope, aspect, plan curvature, profile curvature, elevation, land use, geology, distance from the road, distance from the rivers, distance from the fault, topographic wetness index, and drainage density in the GIS environment. The landslide susceptibility map of the studied area was prepared using three methods of random forest algorithm, maximum entropy, and support vector machine algorithm, and using Receiver Operating Characteristics and 30% of unused landslide points in the modeling process. The results of an assessment of the models indicated that the accuracy of the estimated maps prepared by the Support Vector Machine, Maximum Entropy, and Random Forest Algorithm were 86, 75, and 82 percent, respectively. Therefore, it can be stated that the presented maps can play an important role in identifying the slide-prone areas as well as in the implementation of development plans, especially road construction in the studied area. Given the potential of tourism in the catchment area, it is necessary to pay attention to the landslide potential in the catchment.

Landslide is one of significant mass movement which causes normous annual human and sesmic region. Therefore, finding hazardous areas for planning and management of them is necessary. So, in this reasearch, occurred landslide in watershed region of bidvaz dam which has area of 161 square kilometers and 125 landsliding points are surveyed in different hydrological conditions by index model of slope stability. For specified slopes, graiding test was done and geotechnical parameters were attained. Then, by using Arc view and Arc GIS packages and calibrating parameters in SINMAP and incorporating the geotechnical and hydrological parameters with digital elevation model, SINMAP was conducted. After that, validation was done in two common and high hydrological conditions including rainfalls with a return period of 2-10 years and 20-50 years Respectively. Results showed that the performance of model in simulation of landslides is not appropriate at common hydrological cases. However, increase in hudrological humidity condition, performance of model improves. In calibration and validation, prediction of model increase 21 % and 26 %, respectively. Therefore, utilizing SINMAP model in high hydrological humidity conditions can be useful for choosing dangerous ways according to the landslide map.

The climate change over the past few decades, and consequently decrease in the precipitation, along with the population growth in different regions in Iran have led to an increase in demand for water for domestic agricultural, industrial, etc. consumption. This has led to uncontrolled exploitation of groundwater resources, causing severe decrease in the groundwater level. Artificial recharge technique is one of the methods to compensate for the groundwater deficit, especially in arid and semi-arid regions. Selection of suitable sites before artificial recharge can help improve the efficiency of the project and ensure its success. Having in mind the problems related to decrease in groundwater resources in Tehran due to the increasing population and the expansion of industry and agriculture. This study aims to identify and zoning of regions that are suitable for artificial recharge of groundwater in Tehran Province. The GIS can help determine such regions more precisely, faster, and with better results. For this purpose, the present study integrated GIS and Fuzzy AHP to weigh and combine factors that play a positive role in artificial recharge, such as the depth and changes in the groundwater level, precipitation, drainage density, elevation and land slope, distance from fault, distance from river, geological properties, and land use. After investigating the views of experts about the binary comparison of the criteria, and prioritizing them using AHP, it was found that the hydrological properties were the most effective criteria for the subject under study. Results indicated that 6.2% and 15.75% of the entire area of the region under study are very suitable and suitable for artificial recharge of groundwater, respectively. Very suitable regions are mostly located in the east of the province, with suitable geologic formations, short distance from river, and predominant rangeland and agricultural land use. They also, have a very low and decreasing groundwater level.

Climate conditions are the most important factors affecting human activity on short- and long-term scales. The study of the effect of climate conditions on human life and behavior investigate in the branches of science called human bioclima. The condition of comfort is a set of conditions that, in terms of heat and humidity, at least 80% of the individuals randomly selected and placed in those conditions have mental judgment of comfort. Climate comfort models are considered as useful tools for illustrating the interactions of environmental stressors and human responses that are expressed in terms of empirical classification. In the present study, we try to determine the spatial and temporal patterns of climatic comfort in southern Iran and the ability of bioklima model indices to detect it.  In this research, the methods of analysis of bioclimatical indices have been used. Initially, monthly average temperature, relative humidity, wind speed, water vapor pressure and cloud cover data of 39 synoptic meteorological stations in southern Iran were collected from the Meteorological Organization during the period 1988 to 2017. In the next step, the above datas was arranged by using Excel software. Then with using of biklima software comfort indices (UTCI, PHS, SST PST, STI, and HIS indices) calculated and graphs are plotted by using Excel software. In order to zoning the studied area, we using the correlation between bioclimatic indices and height in ArcGIS software and in the Geostatical Analysis.The bioclima model was developed by Professor Christoph Blansky in 2003 that includes various indicators of human bioclimate. By using this model, we can calculate 60 variables related to biomclimate and thermal physiology. Its input data includes two groups of meteorological data and data related to human physiology conditions.  Analysis of the outputs from the implementation of the bioklima model shows how the heat sensation in different months at each station is based on the six indices of the bioklima model separately. Qualitative evaluation of the results of the bioclima indices in the South & southwest in spring shows that the output of the SST and PST indices are more consistent and the STI and HSI index have the least consistency with other indices and because their output reflects geographical realities, especially topographic diversity. On the contrary, PhS, SST and PST indices reflect the better picture of climatic comfort at this season. A qualitative assessment of the results of the benchmark indices in the study area in the fall season shows that the SST indices reflects a better picture of climate comfort. On the contrary, HIS, STI and PhS indices are not fully effective in reflecting climate realities. The results of this study indicate that HSI and STI indices are not suitable for reflection of climatic comfort conditions in winter. Conversely, the pattern of zoning from the SST index shows that this index is more effective in reflecting the regional climatic realities. Discussion and In this study, by using the six bioclimatic indices of the bioklima model, the bioclimate condition of south & southwest of Iran was analyzed at monthly and seasonal in 7 southern provinces of Iran. The results of this study show that spatial and temporal patterns of thermal comfort do not correspond to each other. Increasing the time of climate unhealthy conditions in the warm season of the year and Low altitude areas & lower latitudes reveals the combined effect of these factors in creating the above conditions. Maximum annual thermal comfort conditions in southern Iran is during the cold period of the year. Also, seasonal patterns indicate that there is maximum comfort in the high areas and Zagros region at period of warm. This condition confirmed in southern coasts and the Khuzestan plain in cold period of the year. Seasonal evaluations of the thermal comfort by apply of six indices of the bioklima model are not match with together, therefore application of these indicators are requires localization and calibration.

Drought is recognized as a slow, creeping phenomenon that can accelerates the expansion of desertification in different regions especially in arid and semi-arid area with its effect on the agricultural sectors, water resources and vegetation cover. Positioning of Iran in the world arid and semi-arid belt is exposed to drought and water crisis and desertification. Drought monitoring and zoning with the goal of planning and water resources management are very important in different area especially in arid and semi-arid regions. One of the methods for drought study is monitoring and mapping of drought using meteorological indices and Geographic Information System techniques. In order to investigate and drought monitoring in present research during the period of 29 years (1989 to 2017) in Qoum-Kahak study region we used annual rainfall data from four stations (i.e. Qoume, Kahak, Koohsefid and Janatabad) which are located inside and around the Qoume-Kahak catchment. Then we applied meteorological indices such as Standard of Precipitation Index (SPI), Percent of Normal Index (PNi), Deciles Index (DI), Chinese Z Index (CZI) and Z-Score Index (ZSI) for drought monitoring during the study period. Afterward for drought mapping, four interpolation methods included Simple Kriging (SK), Ordinary Kriging (OK), Inverse Distance Weighting (IDW) and Radial Basis Functions (RBF) were used and compared. After determination of best method in each year, yearly drought map were produced based on the best interpolation methods in ArcGIS software environment. According to the result of meteorological indices the near-normal class occurred more than other classes in study stations. SPI results indicated that Jannatabad and Kahak stations have the highest and lowest frequency of extremely dry class during the study period, respectively. The most extremely dry class with the SPI of -2.35 was occurred in Janatabad station in the year of 1997-98. Based on the results of PNI and ZSI indexes there was no occurred extremely dry class in all stations. The result of PNI index indicated that Koohsefid station has the highest frequency of very dry class between all stations. According to the result of CZI index the extremely dry class was occurred only for Kahak and Janatabad stations in the water years of 2016-2017 and 1997-98 respectively. Extremely wet class was occurred also for Kahak and Janatabad stations during the water years of 2009-2010 and 1992-1993 respectively. The results of DI index illustrated that the class of very much below normal was occurred in Qoume and Koohsefid stations 4 times and in Kahak and Janatabad stations 3 times during the study period. We applied geostatistical analyst tools in ArcGIS software to produce zoning maps for rainfall and drought. Its result showed that ordinary kriging with RMSE of 28 mm is the best method to create rainfall map. Accordingly mean annual rainfall was 136 mm/year during the study period. We selected SPI index for drought monitoring and zoning because of according to the results of all meteorological indices,only for this index the extremely and severely drought classes for all stations have occurred Simultaneous with the minimal rainfall during the study period. Afterward we compared different interpolation methods to produce drought zoning maps for each year during the study period. Based on the results of RMSE ordinary kriging and simple kriging were the best methods for most years. The results of all meteorological indices suggested that in all stations the near-normal class has the most frequency during the study period. According to the results of SPI and CZI indices, the highest (7 years) and lowest (3 years) number of drought were occurred in Kahak and Jannatabad stations, respectively. While according to PN index the highest (9 years) and lowest (3 years) number of drought were observed in Qoum and Jannatabad stations, respectively. Also the results of PN index illustrated that there was no wet period during the study period. According to the ZSI Index, the extremely dry class did not occurred in the region and the ZSI results about the frequency of extremely wet class was similar to the SPI results. The results of drought zoning indicated that in the years of 1990, 1995 and 1997, most of the region has been in a state of extremely to slightly drought. In the years 2010 and 2017, slight to sever drought classes were occurred in most parts of the study area and the slight wet class was observed in the eastern part of region. While during the years of 1992, 1996 and 2009 whole of the study area has experienced moderate and severe wet conditions. In the year of 2011, half of the study area which is located in the eastern part of the region has been in a state of moderate wet condition.

Landslide causes many social and economic losses in many parts of the world every year. These losses can be greatly reduced by using appropriate management measures such as mapping landslide susceptibility mapping in the basin. The aim of this study is landslide susceptibility mapping using Mahalanobis distance in the Latyan catchment. First, a total of 208 cases of landslides identificated and geo-referenced using geographic information systems based on an interpretation of aerial photographs and extensive field surveys and provided a landslide inventory map. The map of 12 factors, including rainfall, land use, distance to fault, distance from river, distance from road, lithology, altitude, slope, aspect, plan curvature, Peak Ground Acceleration and topographic wetness index as the most important factors in landslides was prepared. Then the correlating each factor and the landslide was examined. Finally landslide susceptibility zoning map was provided based on the Mahalanobis distance in Latyan catchment. To evaluate the results, the ROC and chi-square tests were used. The results show more than 80 % of the catchment located in range of high and very high susceptibility classes and need to suitable management operations. AUC index (area under the curve ROC) for this model is achieved to 0.896 or 89.6% which represent capability and high accuracy. Chi-square test results also reflect the proper separation of landslide susceptibility classes by model.

Drought is a natural and recurrent phenomenon. It is considered ‘a natural disaster’ whenever it occurs in an intensive manner in highly populated regions, resulting in significant damage (material and human) and loss (socioeconomic). Although drought occurs in areas with different climatic conditions. But more is happening in arid and semi-arid areas with less annual rainfall. In general, droughts occur when the amount of annual precipitation is lower than the average long-term precipitation of the area. In other words, a drought occurs when the amount of water in the water resources of a region is continuously reduced in a continuous period. Climate, hydrology and agriculture drought are different types of drought. Drought is one of the most important natural hazards that causes a lot of damage in different areas each year, and it can turn into famine. Due to variety, complexity and various effects of drought, various indicators for its analysis are used. Accordingly, most of the indicators used in drought monitoring are climate variables such as temperature, precipitation and soil moisture. Today, drought can be accurately analyzed and evaluated using remote sensing data. Accordingly, this research tries to evaluate and monitoring the drought of Sirjan basin using remote sensing data. This is an applied research, which uses NDVI (Normalized Difference Vegetation Index) products of MODIS sensor with a resolution of 250 meters and 16-hour time resolution. It also uses the LST product of this sensor with a spatial resolution of one kilometer and a resolution of 8 days. The data are for June 2000 to 2017. After receiving the data in the first step, Correction of molecular dispersion, ozone and dust absorption, radar correction and solar radiation angles were performed on the NDVI product using the BRDF (Bidirectional Reflectance Distribution Function) model. NASA has made corrections such as georeferencing, radiation calibration, the removal of cloud effects, atmospheric and steam temperatures on NASA's LST product. Then in the next step the VHI (Vegetation Health Index) index was calculated from the combination of VCI (Vegetation Condition Index) and TCI (Temperature Condition Index) indices based on the 18-year time series (2000-2017) in June. Eventually drought zoning maps based on the VHI index were produced in four classes: Severe drought, Moderate drought, Mild drought and without drought. According to the De Martonne method, a large area of Sirjan basin is located in Arid and hyper arid zone. Average rainfall is different, in northern and northeast parts is about 500 mm and decreases to 99 mm in the central and southern parts of the basin. Also, the mean long-term temperature varies from an average of 1° C to the north and north east of the basin to 18°C in its southern parts. Generally, the research findings have been shown that droughts did not have a regular trend in the Sirjan basin. Discussion and Conclusion: Basically, rainfall is the most important factor in the occurrence of drought. The results of various studies worldwide show that satellite indicators along with ground-based meteorological indices can monitor the drought trend effectively. The results of the study showed that the effects of drought on surface temperature and vegetation are different. Moreover, that depends on the various factors such as the type and characteristics of the vegetation and their temperature conditions. VHI is one of the most important combination indicators for assessing the severity of drought in different regions. Because, this indicator can simultaneously shows the effects of drought on vegetation and land surface temperature. The results showed that 2007, 2008 and 2017 have had the highest drought severity. So, in 2007, with a maximum drought, 4377 Km² of the study area suffered from severe drought. The area under severe drought in 2017 and 2008, was 3795 and 3782 Km², respectively. Accordingly; the lowest level of drought in the region was estimated in 2009, where 4195 Km² of the studied area were located in a without drought zone. In this year, only 4 Km² of the study area was located in a severe drought zone. In general, the use of drought intensity zoning maps that are produced using Combined Indicators helps in drought monitoring and planning to reduce drought losses.

Drought is one of the atmospheric phenomena that adverse effects on various sectors environmental, economic and social gradually identified and is tangible and that is the reason it is also called creeping disaster. In this study, droughts zoning for five years consecutive caspian plain with using spi and czi indices with Arc Gis for stations synoptic: Astara, Inchebron, Bandar anzali, Babolsar, Ramsar, Rasht, Ghaemshar, Gafarhaji, Gorgan and Noushar collected. For this purpose, total rainfall for 25 years 1986 to 2010 was considered .The results indexs show that, most wet year for stations Astara with index respectively 2.6 and 2.22 in 2000 year and most dry year for station Babolsar in 2010 year with index respectively -2.86 and -2.39. Based maps drought five year in 1990 most of the drought in the eastern part of the caspian plains and other areas faced with lower rates. in 1995 year the drougt has led to the central plain and in 2000 year area west and central plains experienced drought magnitude is different. in 2005 year drought with differeant magnitude occurred in all parts of the plains and also in 2010 year all part plain caspian withness drought and process drought has decrease in east to west plain caspin.

This research aims to study temporal and spatial variations of groundwater quality of Marvast Plain to manage these recourses. For this purpose, total anion and cation, a percentage of sodium, Ca, Cl, EC, HCO3, K, Mg, Na, pH, SRA, So4, TH parameters were prepared and a statistical period of 14 years (1377_1390) was selected using the evaluation of incomplete data. Semivariograms and cross semivariograms were potted to perform geostatistcal methods and the best model was selected with regard to more R2, less RSS and a strong spatial structure, and an exponential model was fitted for most parameters. Then, interpolation methods, such as Radial Basis Function (RBF), Inverse Distance Weighting with powers of 1 and 2 (IDW), Kriging and coKriging were used. The most accurate method was selected for parameters using cross validation method and error estimation indexes such as Mean Absolute Error (MAE), Mean Bias Error (MBE) and Root Mean Square Error (RMSE). The results showed that coKriging is the best method for estimating most parameters (total anion and cation, EC, Cl, Ca, K, Mg, Na, TH). Kriging is defined as the most suitable method for estimating SRA, So4 and pH, and RBF and IDW with power 1 are the most fitted methods for a percentage of Sodium and HCO3, respectively. The results showed that the density of most parameters increases from west to east and south to north of the study area. Also, decreasing underground water quality is observed over the time.

Introduction Today, a significant portion of the water consumption in Iran, especially in the drinking sector, is provided by water resources. Exploitation of groundwater resources requires knowledge of the quantitative and qualitative status of aquifers. By determining the chemical quality of groundwater, an estimate of the health status of these water resources can be obtained and, depending on its state, the type of use is determined. In this regard, direct and indirect methods can be used to understand the qualitative characteristics of water. Direct methods, despite their high precision, require a high size of observational data, involves substantial time and cost. Hence, numerous indirect methods have been developed for simulating natural systems and estimating their parameters using a computer based on complex calculations. The main advantage of these methods is the ability to learn time series and prediction. One of these methods is modeling or hydrological simulation. The modeling of groundwater quality is an important tool for planning and decision-making in the management of water resources. The goal of this research is to identify the ability of intelligent model of Support Vector Machines (SVM), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) for modeling groundwater quality variables (EC, SAR, TDS, and TH) in Gero plain and zoning these variables. Therefore, it can provide an appropriate management tool for controlling quality parameters for drinking and farming.
Material and methods In this study, data from 14 wells over the 2008-2016 period was used in order to model the variations in quality variables of Gero plain groundwater. The observed values for Na, Mg, Ca, SO4, Cl, and HCO3 are considered as independent variables and values of EC, SAR, TDS, and TH are considered as dependent variables. An SVM, an ANN, and an ANFIS design were used to model groundwater quality. Input data are randomly divided into two sets such that 80% of data are assigned to the training set and the remaining data (20%) form the test set.
Results Results showed that the ANFIS system had the best performance in the estimation of EC (R2 = 0.99, RMSE=109.13, CE=0.99), SAR (R2 = 0.98, RMSE=0.28, CE=0.98), and TH (R2 = 0.99, RMSE=0.49, CE=0.99) among considered methods for the modeling of groundwater quality. Results also indicated that the ANN had the best performance in estimating TDS (R2 = 0.99, RMSE=109.13, CE=0.99). Furthermore, Schoeller and Wilcox water quality classifications, for drinking and agricultural water, were respectively employed to perform groundwater quality zoning based on outcomes of the considered methods. According to Schoeller classification, TDS has three classes: inappropriate (21.1%), bad (74.59%), and non-dirking (4.31%) and TH variable has four class: good (0.84%), acceptable (23.48%), inappropriate (67.55%), and bad (8.16%). According to Wilcox classification, EC has three classes: excellent (9.41%), good (89.79%), middle (0.8%) and SAR has two classes: excellent (19%) and good (81%).
Discussion and Conclusion ANFIS for a better estimation of EC, SAR, and TH variables outperforms two models of ANN and SVM. The ANFIS system, using the if-then rules, describes that these rules are implemented in a network structure that can be used for learning algorithms used in ANN. Due to this structure, the fuzzy-comparative neural network model has more transparency for analysis and interpretation. The zoning of qualitative variables (TDS and TH) based on the classification of Schoeller drinking water showed that in the TDS variable, the groundwater quality has three classes: bad, inappropriate, and non-drinkable, with the most inadequate plain, southeastern plain bad status and the west of the plain has a terrible situation. The TH zoning map presents that the plain is in good, acceptable, inappropriate, and bad classes. The most part of the plain is in the inappropriate class, the west of the plain in the bad class, and the southeast plain is in an acceptable class. The results of zoning the variables EC and SAR based on the Wilcox agricultural classification showed that groundwater quality is acceptable four agricultural purposes. Therefore, it is essential to take measures to improve the quality of drinking water in the region.

The ultimate aim of any research on the mass movement processes is to prepare zonation map and classify area into different degrees of hazard in order to mitigating related damages. This study was undertaken using F-AHP & GIS within Nekaroud watershed in Mazandaran province. Pairwise comparisons showed that sub-criteria’s Slope (>30%), elevation (> 1700 m), distance from fault (0-200 meter), distance from river, residual land-use and precipitation (>600 mm) have high weight than others in their group which lead to increasing occurrence of landslides. In the study area much amount of lime stone was found which is effective in reducing landslide. Major parts of central areas of watershed are prone to the most dangerous and high frequency landslides, other parts of watershed classified into medium and a little part is in low danger class.

Temperature is one of the most important meteorological parameters that can be used in many case studies. This parameter is of particular importance in the study of climate change and agriculture, So that climate change and rising temperatures is considered one of the most important environmental issues of human. Zoning of this parameter and study its changes can be effective in the correct management of soil and water resources and supply water requirement. This research studies the consequences of the climate change on the rainFall pattern in Ardebil province in the next three decades using the output data of the AOGCM model (hadcm3) under A1B scenario. Finally, the zoning of the temperature patterns changes were obtained in these periods. The results showed that there will be a significant upward trend in the temperature of the province. Moreover, in the third decade, Kosar station would be in the best condition with 0.35-3.13°C increase in temperature, and Sarein would be in the worst condition with 0.68-3.84°C increase in temperature.

The aim of this study is determination of probability of occurring precipitation status under the condition temperature status and. zoning map preparation. For this purpose, the daily temperature and precipitation data from 13 synoptic stations were used in Fars province which includes at least the period of 20 years. At first were determined different scenarios of temperature and precipitation. In the next step, daily common frequency of temperature and precipitation events were counted and presented in matrix. In the following, common probability matrix of temperature-precipitation events was calculated. Finally, probability of precipitation under the condition temperature status was calculated for each station. Then 9 probability characteristics of 13 stations were analyzed clustery based on partial Euclidean distance indicator by wards method and their dendrogram were plotted. In the final step, zoning and probability maps was prepared using GIS software. The results showed that based on conditional probability characteristics, study area are divided in to three zones. Also, average results of probability of precipitation under the condition temperature status indicate high chance of days without precipitation and warm than other modes. In all three study zones, the days with high precipitation and low temperature are in second degree of chance. The minimum chance of occurrence, related to days with heavy rain and warm.