حسن فتحی زاد

Groundwater quality can be evaluate by comparing measured quality parameters to prepared standards. This method although looks simple, but it doesn't show a comprehensive view of water quality condition in arid and semiarid areas. In current research after calculations of groundwater quality of Mehran plain using IRWQI, the ability of different geostatistical methods have been investigated for quality zoning.

Based on the importance of groundwater resources in Mehran plain, 17 piezometer wells were selected. Afterward, sampling and analysis have been done during February 2020. For calculating IRWQI index, all measured quality parameters were weighted based on the roles in groundwater pollution. Pearson correlation coefficient was used to evaluate the correlation between data and IRWQI index. Then, for zoning groundwater quality different geostatistic methods including Inverse Distance Weighting, Global polynomial interpolation, Radial Basis Function, Local Polynomial Interpolation, Ordinary Kriging, Simple Kriging and Universal Kriging have been used.

The results show a significant correlation between water quality index and EC and SAR parameters at the level of 1 and 5%. Results show that, based on IRWQI, the quality of groundwater in the plain is very good by mean index of 100. In another hand, the relationship between groundwater quality parameters and IRWQI showed that EC and SAR have significant correlations. Also, based on the results, IDW has the minimum error by 10.10, 12.65 and 0.77 for MAE, RMSE and R2, respectively, comparing to other geostatistical methods and it has been selected as the best method for Mehran plain groundwater quality zoning.

Groundwater zoning map also indicated that the very good class with the area of 12429 hectare (%69) covered the maximum area of the plain and shows a very good quality of groundwater in whole plain.

In recent years, groundwater discharge is more than recharge, resulting in a drop-down in groundwater levels. Rangeland and forest are considered the main recharge areas of groundwater, while the most uses of these resources are done in agricultural areas. The main goal of this research is to use machine learning algorithms including random forest and Shannon's entropy function to model groundwater resources in a semi-arid rangeland in western Iran. Therefore, the layers of slope degree, slope aspect, elevation, distance from the fault, the shape of the slope, distance from the waterway, distance from the road, rainfall, lithology, and land use were prepared. After determining the weight of the parameters using Shannon's entropy function and then determining their classes, the final map of the areas with the potential of groundwater resources was modeled from the combination of the weight of the parameters and their classes. In addition, R 3.5.1 software and the randomForest package were used to run the random forest (RF) model. In this research, k-fold cross-validation was used to validate the models. Moreover, the statistical indices of MAE, RMSE, and R2 were used to evaluate the efficiency of the RF model and Shannon's entropy for finding the potential of underground water resources. The results showed that the RF model with accuracy (RMSE: 3.41, MAE: 2.85, R² = 0.825) has higher accuracy than Shannon's entropy model with accuracy (R² = 0.727, RMSE: 4.36, MAE: 3.34). The findings of the random forest model showed that most of the studied area has medium potential (26954.2 ha) and a very small area (205.61 ha) has no groundwater potential. On the other hand, the results of Shannon's entropy model showed that most of the studied area has medium potential (24633.05 ha) and a very small area (1502.1 ha) has no groundwater potential.

Introduction :

Groundwater is one of the most important natural resources in the world. In the current situation, a significant part of Iran's consumption in the drinking sector is provided by underground water sources. In the last 20 years, salinization and reduction of groundwater quality have become one of the serious environmental problems around the world. This issue adds to the importance of proper management of groundwater consumption. Proper management of groundwater consumption is not possible without having sufficient knowledge of the distribution and expansion of saline and fresh groundwater and determining the processes influencing its evolution. Atmospheric precipitation and surface water, passing through different geological formations, dissolve the salts in them. Therefore, the chemical quality of underground water depends on the type of formation that passes through its empty spaces. Also, the time the water stays in the basement has an effective role in the quality and salinity of the water, because the longer the water stays in the sediments, the saltier it becomes.Groundwater in arid and semi-arid areas, especially in Yazd province, is the main and reliable source of water supply for various uses due to low rainfall and lack or absence of surface water sources. In order to check the condition of underground water, it is necessary to check the concentration and type of solutes in the water. One of the factors affecting the quality of underground water in Yazd province is the existence of destructive geological formations. Therefore, the purpose of this research is to investigate the relationship between the quality of underground water in Yazd province and the type of formations.

Yazd province, with an area of ​​about 737.54 square kilometers, is located in the central part of Iran's central plateau, between 29 degrees and 52 minutes to 33 degrees and 27 minutes north latitude and 52 degrees and 55 minutes to 56 degrees and 37 minutes east longitude. Yazd province is bordered by Semnan and Isfahan provinces from the north and west, Khorasan Razavi province from the northeast, Kerman and South Khorasan provinces from the east, Fars province from the southwest, and Kerman from the southeast. It varies from about 666 meters above the level of the open sea (Zarin sand desert near Aqda) to 4075 meters (Shirkoh peak). The present research is about investigating the impact of geological formations and units on the quality of underground water in Yazd province based on the statistics, information, and reports available in the Geological Organization using a 1:100000 geological map. In order to check the quality of underground water samples, 217 wells and aqueducts of Yazd Region Water Company's monitoring network in July 2013, the measured parameters of cations (sodium, potassium, calcium and magnesium), anions The main elements (bicarbonate, chloride and sulfate) present in water, electrical conductivity (EC), dissolved solids (TDS), sodium absorption ratio (SAR) and pH were used. To check the quality of underground water, sampling has been done in 18 regions of Yazd province. RockWorks 14, ArcGIS 10.3 and Excel software were used to draw and prepare maps.

To check the groundwater type of 217 sampled points in Yazd province, the average data of each region was used. By using the main ions, samples of the type of underground water in each region have been obtained in such a way that by separating the ions and their amount in each region and then implementing them in the Piper diagram, the type of groundwater in the region is obtained. As it is clear in Piper's diagram, the predominant type of underground water in Yazd province is chloride+sulfate and calcium+magnesium. These types cover the largest area in the plains and playas, which have limestone and marl. There is also carbonate type in the region due to the presence of granite formation heights that have better quality water. The interesting point in this graph is that none of the samples are in the fresh water category. The Piper diagram shows that the groundwater of the studied area is located in the hard and completely hard water type due to the existence of terrace deposits and new low-altitude foothill alluvial cones, which cover about 3,383,512 hectares of the entire area of ​​the region. According to the Wilcox diagram of the study area, C2S2 and C4S2 classes have the lowest and highest shares with 0.46 percent and C3S4 and C4S4 classes with 32.26 and 56.22 percent, respectively. According to the results of the Wilcox diagram of underground water in Yazd province, the water quality of the region is too salty and unsuitable for agricultural use. The results show that the high amount of salts is one of the problems of the water that is used for irrigation in this area. High concentration of salt in irrigation water can reduce the growth of crops or even prevent the growth of some crops, which results in soil decomposition and groundwater pollution. In such water, the growth of plants and their quality will be improved by increasing irrigation for washing or using intermittent water with low salinity and good drainage.

If there is a trend of pollution and over-harvesting of the underground water table in the region, in the next few years, with the increase of TDS in the region, the condition of the water in terms of drinking will become worse. The best quality of water for drinking is in the central and southwestern regions where the normal gneiss granite formation with eye grain structure is located in the mountainous area of Shirkoh, and the further we move away from the center, the worse the water quality is. The formation of these areas is of the type of terrace deposits and alluvial cones of new foothills.

Groundwater resources are one of the main natural resources that support socio-economic development of countries (Siebert et al., 2010). Management and protection of groundwater resources is of great importance in countries such as Iran, which are located in arid and semi-arid regions without surface water resources. Therefore, resource conservation, in addition to short-term and long-term planning, is essential to optimizing their productivity (Abu-Khalaf et al., 2013). These natural resources face a variety of issues that threaten their sustainability, such as the effects of climate change, human activities and natural processes (Alabjah et al., 2018; Baghvand et al., 2010; Burri et al., 2019; El Asri et al., 2019; Houemenou et al., 2020; Mountadar et al., 2018). Considering the great importance of recognizing the quality characteristics of groundwater in desert areas and the need for its proper management, predicting groundwater quality for the management and exploitation of water resources in the Yazd-Ardakan plain seems urgent. Therefore, the aim of this study was to evaluate and compare the efficiency of artificial neural network and random forest models in predicting EC, SAR, SO4- and TDS values. In this research, modeling will be done based on the relationship between environmental (auxiliary) data and groundwater quality parameters.

The study area is spread over ​​482900 ha of land, which in terms of position is 53° 08´ 36˝ and 54° 85´ 32˝ E longitude, is located in the central plateau of Iran and the central part of Yazd province. The maximum height of the area is 2677 m and the minimum height is 997 m from the sea level. To evaluate the quality of groundwater sample, 201 wells of the monitoring network of Yazd Regional Water Company in 2016, the measured parameters EC, SAR, SO4- and TDS were used. The factors of environmental (auxiliary) data in this research include geological data, land use, vegetation indices, soil salinity indices, derivation of digital elevation model, distance from mines, distance from road, distance from river, distance from residential areas, rainfall and population.After preparing the groundwater quality parameters and environmental data, the values of EC, SAR, SO4- and TDS were predicted using artificial neural network and random forest models. In order to validate the random forest model, the cross-validation method (10-fold) was used in R statistical software. In this method, the data is divided into 10 parts. 9 parts of the data are used for modeling and the remaining part is used to validate the obtained model. MAE, RMSE and R2 statistical indices were used to evaluate the efficiency of artificial neural network and random forest models for groundwater parameters.

In this research, in simulating EC, SAR, SO4- and TDS parameters, the best structure obtained from 100 repetitions of artificial neural network learning has 2 hidden layers and 10 hidden neurons in each layer. The results of random forest sensitivity analysis show the high importance of the parameters extracted from the DEM. The use of satellite image data to investigate the groundwater quality parameters is also a convenient and cost-effective method. Furthermore, combining satellite data with DEM to investigate groundwater quality parameters and their zoning makes the results more efficient and increases its accuracy. A very important point in the analysis of parameters is the important role of parameters such as distance from the road and distance from the mines. This indicates the direct effect of man-made environmental factors on the groundwater quality of the study area. The results of modeling the EC, SAR, SO4- and TDS parameters using artificial neural network and random forest models show the appropriate accuracy of the artificial neural network model in predicting these parameters. Accordingly, the neural network model has predicted the parameters of EC, SAR, SO4- and TDS of groundwater with coefficient of determination of 0.82, 0.92, 0.92 and 0.97, respectively, while the model Stochastic forest has predicted EC, SAR, SO4- and TDS parameters of groundwater with coefficients of determination of 0.37, 0.65, 0.80 and 0.68, respectively. Based on the obtained results, it can be concluded that the artificial neural network model has a higher accuracy than the random forest model in predicting groundwater quality parameters. EC, SAR, SO4- and TDS parameters have the highest values in the north, center and southwest, and the lowest values in the southeast and southwest.

It can be said that the technique is highly efficient for estimating EC, SAR, SO4 and TDS parameters in Yazd-Ardakan plain as long as a proper and a sufficient number of input elements, a proper and compatible artificial neural network, and an appropriate calibration are used.

Strategic management process including setting objectives, Identifying strengths and weaknesses (internal factors), identifying opportunities and threats (external factors), setting long-term goals and determining various strategies, calculating and evaluating performance. The aim of current study is to evaluate the efficiency of SWOT model in preparing strategic plan for integrated watershed management in Meymeh watershed, Ilam.

The current research has been done in three subwatersheds of Meymeh watershed. This watershed has been selected as pilot watershed study of Ilam province in line with the National Megaproject on Integrated Watershed Management which has 20584 ha area and located in Dehloran county, Ilam province. Data collection was done in different kinds of library and field information and after discussion to scientists and experts, weakness, strength, opportunities and threat of the catchment are identified. In this research, 22 strength and opportunities as advantages and 25 weakness and threats as limitations in the studied watershed are specified. All of this characteristics are used for data analysis and preparing strategies using SWOT model. For this analysis, 30 questionnaire are filled by academic, non-academic and local experts, then for weighting and scoring of factors, AHP method is applied for factor classification. Finally, Using SWOT strategic plan for integrated watershed management is suggested and for strategies priorities, QSPM planning matrix has been used.

Results show that between different strengths, "permanent rivers" with 0.364 score as the most important strength and "no applied agriculture and natural resources planting pattern" with 0.312 score as the most important weakness of the catchment have been identified. Also, between opportunities, "involve the local communities in natural resource projects" with 0.351 score as the most important opportunity and "The negative consequences of climate change due to the definite impact of the province's natural resources" with 0.507 score as the most important threat of the studied catchment have been recognized.

Investigations of IFE, EFE and weights of strategies indicated that in future planning, opportunities in the catchment should be taken in account.

The complex relationship between soil and vegetation is difficult to be simulated by mathematic and statistical models. Since soil, vegetation, and atmosphere are interrelated, they cannot be separately compared. As an important basic factor with regard to soil, vegetation greatly affects ecosystems, especially in arid and semi-arid areas. Therefore, the restoration and recreation of this complex phenomenon is much more difficult in arid and desert regions. On the hand, considering the fact that plants generally affect the soil both physically and chemically, it is necessary to know how these parameters interact, the knowledge of which could significantly help restore the vegetation. As a natural, resistant and desert-friendly plant, Capparis spinosa has been known as a multipurpose species in recent years. Because of its deep roots, this species is resistant to wing and its wide canopy can cover a wide range of the soil’s surface and stabilize sand hills. Therefore, it could be an appropriate choice for preserving soil and water, and anti-desertification programs. It could also help control dust storms in different regions. This study, therefore, sought to investigate the effects Capparis spinose on soil characteristics of the Eyne-Khosh desert area in Dehloran, southern Ilam province, offering it to the relevant officials and decision makers as an effective plant for anti-desertification programs. 

As a part of desert areas in southwest Iran, the study area is located in Dehloran city, Ilam province. To investigate the effects of Capparis spinosa on soil characteristics, this field study used satellite images, GIS, laboratory analysis, and the date obtained from a statistical software. To this end, first the species’ distribution map was prepared, using field observation and liner transect for calculation of coverage percentage in the growing season. Then, other characteristics including canopy percentage, soil preservation percentage, and other factors were measures, using sixty-nine species randomly selected for the study. As for investigating the effects of Capparis spinosa on soil’s physical and chemical characteristics, a soil profile was dug, and the intended samples were collected from two layers (surface and subsurface) of fifteen points including the surface soil of the existing bushes (near the stem) and control areas (bush-less), which were then moved to the laboratory (totally 30 samples) where chemical factors including N, K, P, OC, ESP, CEC, Ca, Mg, Caso4, Caco3, Cl, HCO3, pH, EC, and SAR were measured.

The study’s findings showed that the highest values of all chemical factors (N, C, P, K, Na, Ca, C/N) belonged to the bushes’ subsurface soil. On the other hand, there were statistical differences between land use (control areas, Nebka bushes, and natural bushes) and land depth for other seven parameters (OC, ESP, Ca, Na, EC, pH, SAR). There were 5% difference between EC and PH in terms of land use, and 5% difference between CA, OC, and CI in terms of land depth. For physical soil factors, actual soil moisture and saturated soil moisture show a strong relationship to depth and they are more in both depth, comparing to control part, although for the other parameters (clay, silt, sand, field capacity and wilting point) there were not statistical different. It was also found that the presence of the study bushes decreased salinity and increased alkalinity of the soil. Moreover, the highest number of variations of the organic matters of the control points’ deep layers occurred in subsurface layer of under-bushes, indicating that this species had increased the organic carbon of its sub-soil compare to the surrounding areas. The findings also showed an increase of OC, Na, Cl, and HCO3 in under-bushes soil. 

According to the study’s results, Capparis spinosa has significant effects on soil characteristics and may change soil texture, increase the soil’s organic matter, maintain the soil’s moisture, and in general, preserve the soil and control its erosion. It could also positively affect chemical parameters such as decreasing the salinity, increasing the alkalinity, and increasing OC, Na, Cl, HCO3, and nutrients of the soil. Therefore, this species could be an appropriate choice for anti-desertification programs in this region and similar areas.

Nowadays, water scarcity and desertification are serious problems in many parts of the world, which puts the development of vegetation and the growth of agricultural products at serious risk. Therefore, proper management and application of new methods to increase irrigation efficiency and thus improve the utilization of limited water resources is an inevitable necessity. One of these methods that have been considered recently is the use of various additives such as polymer and natural. This study aimed to investigate the effect of natural and artificial superabsorbents on the performance of Prosopis Juliflora in climatic and edaphic conditions of arid areas of Ilam province, especially in Mohsenab plain of Mehran city, and it is an issue that is less addressed in this region and in general in the field of arid and semi-arid regions. In this regard, the effects of natural additives (Humus and Sawdust) and artificial superabsorbent (Nanohursan polymer) on Prosopis juliflora have been compared by comparing the average of thirtheen vegetative and physiological indexes in a completely randomized block design. The results of this study showed that the effect of superabsorbents (treatments) on all morphological and physiological traits of the studied species except for vegetative traits, stability coefficient, and specific leaf area index was statistically significant at p < /em>< 0.05. Therefore, as a general conclusion, it can be said that the majority of growth characteristics of Prosopis juliflora have improved due to the use of the studied treatments, and therefore, the addition of natural and unnatural additives in this region and similar areas is recommended.

The purpose of this research was to investigate the trend of annual changes in Yazd stationchr('39')s meteorological parameters including minimum and maximum average daily temperature and average daily precipitation (1961-2005), as well as the predicted annual mean of these parameters in the three upcoming thirty years of the 2040s, 2070s and 2100s, by the SDSM model, under RCP2.6, RCP4.5, RCP8.5, A2, and B2 scenarios. Accordingly, by using the coefficient of determination and the MAE, R2, RMSE indicators, we evaluated the data generated by the SDSM model in comparison with the observed data in the base period. The lowest value of R2 based on the calibration and validation of the mean values of observed and simulated SRES was obtained for precipitation (86 and 80%). In terms of the R2 evaluation index, the accuracy of the small-scaled results of the minimum and maximum average temperature values was more than that of the average precipitation; however, in terms of the MAE and RMSE evaluation indicators, the accuracy of the small-scaled results of the average precipitation was higher than that of the minimum and maximum average temperature values. Subsequently, HadCM3 large-scale climatological data was used to predict the future periods (2010-2100). The results indicated that the temperature was raised in all months and seasons and the precipitation was decreasing in most of them, thereby confirming that the climate was changing in the studied region.

The number of stormy days is determined by various factors such as wind speed, rainfall, soil moisture and so on. The study of this index in the country can be considered in various plans. The purpose of this research is mapping of the number of dusty stormy days in Iran and selecting the best model based on the climatic data of 150 meteorological stations for the period of 25 years (1986-2010).

Dust stormy days’ data of the studied stations were analyzed using variogram curves to represents their spatial correlation. Gaussian variogram (R2=0.96) shows the highest correlation between the data. Then, map of the number of dust stormy days in Iran were prepared using different geostatistical and mathematical methods. For this purpose, several mathematical interpolation methods including Inverse Distance Method (IDW), Global Polynomial Interpolation (GPI), Radial Basis Function (RBF), Local Polynomial Interpolation (LPI), and geostatistical method of Kriging were used. To select the best interpolation method among several geostatistical and mathematical methods, statistical indicators of Root Mean Square (RMS) and correlation coefficient between observed and predicted data were used.

Results show that the highest correlation between predicted and observed data (R2 = 0.74) was found in kriging indicator method. The southeast and southwest of the country have the highest number of dust storm days.

High number of dust stormy days in the southeast is resulting from drying of Hammon lakes and blowing of 120-day winds in Sistan plain, and entering of dust from Arabic countries form the direction of southwest. North part of the country has the lowest number of dust storm days.

In recent years, due to the expansion of industrial activities, the concentration of heavy metals in the environment as well as foods has increased. Heavy metals are dangerous because of their bioaccumulation. Regarding the importance of contamination of heavy elements of the soil, the present study aimed to provide the spatial distribution of heavy metals and its relationship with land use in the Yazd-Ardakan plain, Iran. First, 201 soil samples from depths of 0 to 20 cm were sampled using the hypercube method, and the total concentration of iron, manganese, nickel, lead, and zinc elements were determined using Analytical Jena-novAA300 atomic absorption device. Then, to convert point data to surface data, geostatistical methods of IDW, GPI, RBF, LPI, and Kriging were used. The land cover/use map of the Yazd-Ardakan plain in 2016 was mapped using an object-oriented classification method. Results of the relationship between heavy metals concentration and land cover/use showed that the agricultural lands and gardens and sand dunes with the mean of 0.950 and 0.836 ppm had the highest and lowest iron concentrations. The highest mean concentration of manganese was related to the residential land (1.821 ppm) and the lowest mean of rocky terrains (1.083 ppm), the most average for poor rangelands and bare land was (0.302 ppm), Residential areas had the lowest nickel concentration (0.192 ppm). The highest mean of lead metal in agricultural land and gardens, as well as residential areas (1.465 and 1.373 ppm, respectively) and the lowest, mean in rocky terrains (0.925 ppm). Agricultural and gardens areas, and residential lands, have the highest mean of zinc concentration (0.583 and 0.552 ppm, respectively), and the rocky terrain has the lowest zinc concentration (0.342 ppm).

Over the past decades, due to increased population and consequent increase in the need for food, we have seen extensive changes in land use, and in particular, the increase of agricultural lands. The aim of this study was to evaluate the changes in land use in the Bartash plain in Dehloran city of Ilam province during 26 years from 1988 to 2014 using the object-oriented approach. To accomplish this research, the necessary corrections were made after the acquisition of Landsat TM (1988), ETM + (2001) and Landsat 8 (2014) satellite images, and then, using the object-oriented method, the land use map was prepared for the three time periods. The results of the evaluation of the accuracy of the produced maps show that the highest accuracy and Kappa coefficient with the values of 90 and 95% correspond to the image of 2001, and the lowest them with the value of 80 and 90% was related to the image of 1988. Total accuracy and Kappa coefficient in the image of 2014 with 90% and 92%, respectively show a good accuracy. The results of land use change trend showed that the land use of the fair rangeland had the most changes with a decrease of more than 21 thousand hectares. Agricultural lands are in the next place, showing an increase of over 15,000 hectares (twofold) that could be due to the increase in population and the availability of adequate water resources in this area. The land use of poor rangelands also shows an increasing trend of 1.5 fold, indicating the degradation of fair rangelands. The saline lands initially show an increasing trend but then show a decreasing trend due to converting to agricultural lands. The overall accuracy (900-90) and kappa coefficient (95-90) indicate the high accuracy of this method in determining the land use.

Detection and prediction of changes in landscape, is necessary for the maintenance of an ecosystem, especially in developing countries with rapid changes and without planning. The object of this research, is monitoring landscape changes in past and it’s simulation for future using Markov chain Consolidated and automated cells (CA-Markov) in arid and semi-arid region of Meymeh Dehloran, Ilam. Landsat satellite images of (TM) 1985, Landsat (TM) 2000 and Landsat (ETM) 2016 were used. Change detection maps were prepared in seven classes of agriculture, Forest, fair range, poor range, rocky protrusions, residential land and salt land using supervised classification ARTMAP FUZZY neural network. Accuracy of the classification landscape maps for 1985, 2000 and 2016, are 93, 95 and 93 percent, respectively. Changes in landscape were predicted for 2030, using Markov chain model and automated cells. Predicted matrix results based on 2001 and 2016 maps showed that in span of 2016-2030, it is likely that 13% of agricultural land, 54% of Forest, 48% of the fair range, 82% of poor range, 55% of rocky protrusions, 52% of the residential land, 93% of salt lands and marsh land converted to other land uses. To validating the model, simulated landscape map of 2016, were compared with satellite image classification of the same year. Kappa coefficient was 87%, which shows the high capabilities of CA-Markov model to simulate landscape changes in arid and semi-arid region of Meymeh Dehloran.

One of the main strategies to achieve the forestry objective and to minimize the environmental crisises, planning use of the forest by taking a natural ability is for users desired. In this research, using the Analytic Network Process (ANP), the criteria of ecological, economic and social, in the BOCR structure frame work in forest habitat of Jouzk-darekhsh (Northern Khorasan) were evaluated and prioritized. The results showed that the sub network of profits with a total weight of 0.55 is located in first priority and sub network of risks with total weight 0.07 in the last priority. In sub network of earnings, revenue increase was in the first priority and the first priorities of the risks sub network also was allocated public acceptance. Among the strategic criteria also social criteria were to assign high weight (0.49) and then economic criteria weighing 0.31 and ecological with weighting 0.19. Finally the results showed that ecotourism options with a total weight of o.62, protection with a total weight 0.19 and option exploitation of ancillary products with a total weight 0.18 respectively in the priority first, second and third. According to the results, the forest can be pushed to the client-side tourist undoubtedly made an impact in preventing the destruction of all the fields, employment and public participation, public participation and respect for the policies followed by the increase in the residents of the area, helping to prevent migration to urban areas and overall development of villages surrounding the forest area will be followed.

The aim of the present study is to investigate the amount of salt in the Musian plain and its relationship with falling water table during the time span of 2001-2013. To conduct this research, statistics from 7 observation wells were used. Then, the zoning maps of EC, SAR, TH and TDS were drawn. In order to clarify the relationship between these maps and the drop in water table levels, the hydrograph and the groundwater drawdown map of the plain were prepared. The results showed that the observed groundwater drawdown in the plain has led to an increase in all four parameters in the studied area so that EC, TH and TDS have exceeded world standards in most parts of the studied area. Low amount of rainfall in the area, successive drought and ultimately, the existence of Aghajary clastic formations can be mentioned as factors influencing the unfavorable quality of groundwater in the plain.

Drought is natural phenomenon’s which can damage were and has a significant loss to human and natural structures. The occurrence of this phenomenon cannot be predicted with complete certainty. The main objective of this study is clustering of drought tradition stations and assessment Climate drought based on the triangular relationship between drought and Hydrometeorology and meteorology variables Such as rainfall, solar radiation and sunshine hours. In this study, using data from meteorological synoptic stations in Iran where twenty solar radiations were recorded, was conducted in the period 1970-2010.The stations studied using Minitab17 software based on the standardized precipitation index solar radiation and sunshine duration, Clustering, Standardized monthly precipitation and from each cluster a station were selected and to examine the relationship between drought and solar variables, and to examine the relationship between drought and solar variables the standardized precipitation same value curves map was drawn and then Angstrom equation variables such as solar radiation and sunshine duration as reference variables was drawn. The results showed solar variables divided into eight different Clusters in all studied stations coefficient of Angstrom equation 0.62-0.96 determinate except Bojnoord, Ramsar and Shiraz are 0.038, 0.067 and 0.36, respectively. The statistical mean period shows most station that started in March and continues in June, July, September, and October and continues in the late of November. In this period, the most sunshine hours and sun radiation are recorded.

Image classification is always one of the most important issues in remote sensing, and the obtained information from image classification is widely used in this field and other applications like urban planning, natural resource management, agriculture, etc. Since the main purpose of processing satellite images is preparing subjective and practical maps, choosing a suitable classification algorithm has an essential role. This paper studies the efficacy of Support Vector Machines (SVM) algorithm regarding satellite image classifications and compares it to artificial neural network algorithm. SVM is a group of classified and observed mechanical learning algorithms, used in remote sensing. In this study, SVM algorithms were employed for land use classification of Meymeh area using ETM landsat data. The classification via SVM was automatically performed by three types of linear Kernel, polynomial, and radial basis. Besides, the performance of this method was compared to that of artificial neural network classification method. Results showed that the average overall accuracy and Kappa coefficient of SVM algorithms, including linear Kernel, polynomial and radial basis, were respectively 9 percent and 12 percent more efficient than artificial neural network classification. Consequently, this study substantiates the efficiency and sufficiency of SVM algorithms in classification of remote sensing images.

Studies related to erosion and soil conservation of litho logical and geological characteristics of catchments have particular importance, Due to their effects. Erosion and sediment production are related to many factors, in which, the sensitivity of geological formation to erosion is the most important parameter. In this study, formations sensitivity to erosion in two sub-basins of Doviraj River located in Ilam Province is compared and their role in erosion and sediment yield has been investigated. In order to study the sensitivity of geological formations the Faiznia (1996) method is used. Results showed that the erodibility of the geological formations has a significant role in the erosion and sediment production, so that, the erosion and sediment yield of the first catchment is 2.5 times as much as the second catchment, because 92.4%of the sub-basin 1, is classified as sensitive to erosion and 7.45% the average class of sensitivity, but in the sub-basin 2, the above amounts are 65.01% and 34.99% respectively. Also, in the sub-basin 1, the Gachsaran formation which covers 6304 hectares (82.09% of the catchment) has the highest frequency and the above figure in the sub-basin 2, is 2166.86 hectares (49.07% of the catchment). Higher erosion and sediment yield of the sub-basin 1 compared to the sub-basin 2 is due to the existence of sensitive formation to erosion, especially Gachsaran formation which has the highest frequency in the study area.

Today, soil erosion is considered as one of main subjects of watershed management at national and global levels. Assessment of decreasing risk of soil and it spatial distribution are one of key factors for successful evaluation of soil erosion. The objectives of research were calculation and modeling of rain erosivity factor in Ilam province using data of number of 28 pluviometry stations during of 23 years period. After that, rain erosivity factor was calculated, map of rain erosivity factor was provided using geostatistical algorithms. Spatial correlation between data of rain erosivity was drawn using Variogram. In order to, studying of erosivity index were used (IDW), (GPI), (RBF), (LPI) and kriging. The best algorithm was selected using statistical indices of Root Mean Square Error (RMSE) and MAE. Results showed that, GPI was the best algorithm with RMSE and MAE equal to 28.93 and 20.95, respectively.

Land use mapping is the basic tools for administrators and land planners. Severd methods have been proposed for land-use mapping. The latest and most important methods is using remotey sensed data for Land-use mapping. The aim of this study was performance evaluation of the pixel-based classification. (Fuzzy ARTMAP neural network and decision tree Methods) and object-oriented classification methods and using Landsat 8 image of 2013 for land-use mapping of arid and sem-iarid regions of Meimeh Ilam. Different land use classes were difined using training samples comperison of classification results of three different methods of fuzzy ARTMAP neural network, Gini decision tree and Object-oriented Show that the object-oriented approach, has overall accuracy of 95.30 and Kappa coefficient of 90.88 , and Gini tree decision and Fuzzy ARTMAP Neural Network methods has overall accuracy of 80.32 and 72.20 and Kappa coefficient of 68.75 and 36.18, respectively thus, object-oriented classification method having a difference in overall accuracy 14.98% and 23.1% and Kappa coefficient of 22.13% and 54.7% has a higher accuracy compared with the Gini decision tree and fuzzy ARTMAP neural network. Map area defined by the three methods of classification, are similar in farmlands, poor rangeland, and urban area. The greatest differences were observed in area of medium rangeland and minimum differences were related to the urban area.

Selection of an optimal interpolation method for estimating the characteristics of the not-sampled points was the main aim of this study, due to the important role of data management. In this study, ordinary Kiriging interpolation models including linear, exponential, spherical, Gaussian were used to estimate the mean annual rainfall of Ilam Province. For this purpose the normality of the data was checked using the Kolmogorov-Smirnov method and then the variogram of each model was calculated and plotted. In continuation, the best spatially fitted variogram between the data was used being compared to the other variograms. For this purpose, the relation between the piece effect and the roof of variogram was used (Co+C). According to the parameters obtained from the fitted variograms, the Gaussian variogram with the 0.33 best fitted the correlation between the data and was used for interpolation. In order to evaluate the efficiency of employed models, the root mean square error (RMSE) and the standard error of results were used. The results showed that the Gaussian Variogram having the lowest estimation error (6.12) and root mean square error (166) were the best model for the interpolation of the data in this investigation. Furthermore, comparison of RMSE with Standard Error (SE) for calculating the amount of expectations demonstrated that the four models gave overestimations.

Rangelands are one of the most important renewable resources and because of their extent and economic, social and distinctive environmental impacts are of very special importance. Unfortunately, in our country, like most developing countries, rangelands have been exposed to degradation for various reasons including the non-systematic management of these resources. Remote sensing technology and satellite data are useful tools in the studies of rangeland and vegetation sciences. One of the applications of satellite data is mapping range land use. The aim of this study was to compare two methods of maximum probability and fuzzy for rangeland zonation. For this purpose, Landsat ETM+ was used; then, after final geometric and radiometric corrections, the final classification map was prepared. According to the results of accuracy of these two methods using the kappa coefficient, the artificial neural network algorithm of fuzzy Artmap with a coefficient of 0.9614 was more accurate than the maximum probability algorithm with a coefficient of 0.8058. Results of this study also indicated that the traditional algorithms of classification such as statistical methods due to their low flexibility, and parametric types such as maximum probability method because of the dependence on the Gaussian statistics model, could not provide optimal results, when the samples were not normal. In this study, ENVI 4.5, Idrisi Andes 15 and Arc GIS9.3 software were used.

According to point measurements of precipitation and lack of coverage for all catchments, prediction of such a data is essential. There are various techniques for rainfall data estimation in areas with no recording data. In general, geostatistical methods are more accurate for precipitation estimation in comparison with classical statistical methods. In this regard, the mean annual precipitations of 97 pluviometer stations in Ilam Province were studied for a 23-years period (1987-2010 years). After collecting the relevant data, the isohyetal map was prepared and variogram analysis was performed. In this study, three types of Kriging including ordinary, simple and general Kriging were used. To assess all models, the root mean squared error with standard error was used. The results showed that the general Kriging method with lowest level of mean error (0.003) and root mean square error (74.97) is the best method for interpolation. Also, comparison of root mean square error with standard error for estimation of the expected values showed that all four models estimate the values more than the expected values. Finally, it is observed that there is a significant exponential relationship between altitude and precipitation until 117.4 km radius distance and after this interval, their relationship is randomly.

At present، changes without land use planning، has become a major problem. Most land use changes، without a clear and logical planning is done with little regard to their environmental impact. Since changes in land use is done in wide areas، the technology of remote sensing tool for monitoring the changes are necessary and valuable. In this study، Landsat images (TM) 1988 and Landsat (ETM +) in 2007 using seven techniques to detect change area of 29،141 hectares of forest area protected Mansht Ilam province are analyzed. Change detection techniques used in this study included the subtraction image، vector analysis، differential NDVI، principal component analysis، canonical component analysis، calculus teleschope and calculus maping. In this study، for determining the threshold the methods that requires threshold the statistical methods are used and accprdingly it is realized that threshold changes in the area of study was located in 1 standard deviation. After determining the threshold change of areas with reduced، decreased، or no changes has been identified. To evaluate the accuracy of change detection techniques، taken from the ground realities through field visits and satellite images of Google Earth، the total accuracy and Kappa coefficient were used. Our obtained findings revealed that the difference between infrared band with an accuracy of 5/99 and kappa 2/98 percent of the technique detection change used in this study had the highest precision and CCA2 precision of 9/71 and kappa 2/65% of have had the least accurate in the detection of land use/ vegetation in the study area.

In this research, sediment production and delivery amount by Darabkola forest roads was estimated using the SEDMODL model. To evaluate the model results, the sedimentation rate in the above roads was directly measured using rainfall simulator. Also, the paired t-test, BIAS, RE and RMSE were used to assess the results. The analysis showed that the rate of sediment production from study road's surface using the SEDMODL model and direct measurement under the rainfall simulation were 420.97 and 341.19 tons per year, respectively, and rate of sediment delivered to the stream with sediment delivery ratios of 42% and 51%, respectively, was about 177.58 and 174.02 tons per year. Also, results of the statistical methods of BIAS, RE and RMSE for the aforesaid model were 0.04, 17.59 and 0.71, respectively, and at 95% confidence level, no significant difference was obtained between the observed and estimated data. Therefore, the aforesaid model has the appropriate accuracy and efficiency to estimate the sedimentation rate of the Darabkola forest roads. It was also found that from among the input parameters of model, longitudinal slope of road, precipitation and sediment delivery factors were the most influential factors in the sediment production and transport, respectively.

Land use/cover maps resulting of satellite images play an important role in assessing the land use/ land cover at regional and national levels. Over the last years, many applications of neural network classifiers for land use classification have been reported in the literature, but afew studies have assessed their comparison. In this study, firstly, geometric correction was performed on ETM+ data. Then, with field surveyings, the various land cover classes were defined and training areas were selected. The main Objective of this study is to compare four artificial neural network methods for land cover classification in Doiraj, Mehran and Sarableh region of Ilam province with various climatic conditions. In this study, we have used four artificial neural networks methods of Fuzzy Artmap, multi-layer perceptron, Kohonen and radial basis function. The results obtained of accuracy assessment of classified images showed that fuzzy Artmap classification algorithm with the overall accuracy 94.84 and kappa coefficient 0.93% have the highest accuracy than other methods. Accuracy overall difference in this approach than multi-layer percepteron method was 11.44 and Kappa coefficient 0.18, Compared to kohonen's 17.30 and 0.23% and rather than radial basis function 31.01 and 0.36%, respectively. In this study, the highest accuracy was related to fuzzy Artmap artificial neural network. Therefore, this study proves the efficiency and capability of fuzzy Artmap neural network algorithm in classification of remote sensing images.