Groundwater contamination risk mapping is one essential measure in groundwater management and quality control. The purpose of the present study is to address this mapping by means of a novel framework, which is more suitable for arid regions than other methods developed in previous work. Specifically, we integrate machine learning tools, interpolation and process-based models with a modified version of DRASTIC-AHP to evaluate groundwater vulnerability to nitrate contamination and to map this contamination in Jiroft plain, Iran. The DRASTIC model provides a tool for evaluating aquifer vulnerability by using seven parameters related to the hydrogeological setting (Depth to water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity), while the criteria ratings and weights of these parameters are evaluated by means of an Analytic Hierarchy Process (AHP). However, to obtain the risk map, the results about groundwater vulnerability are combined here with a contamination hazard map, which we estimate by applying ensemble modeling based, in part, on the occurrence probability predicted from Generalized Linear Model (GLM), Flexible Discriminant Analysis (FDA), and Support Vector Machine (SVM). Our integrated modeling framework provides an assessment of both regional patterns of groundwater contamination and an estimate of the impacts of the contamination based on socio-environmental variables, and is particularly suitable for applications based on limited amount of available data. The groundwater contamination risk map obtained from our case study shows that the central and southern regions of the Jiroft plain display high and very high contamination risk, which is associated with high production rate of urban waste in residential lands and an overuse of nitrogen fertilizers in agricultural lands. Therefore, our work is providing new modeling insights for the future assessment of groundwater contamination, with potential impacts for the management and control of water resources in arid and semi-arid environments.
Barchan dunes are among the most common accumulative phenomena made by wind erosion, which are usually formed in regions where the prevailing wind direction is almost constant throughout the year and there is not enough sand to completely cover the land surface. Barchans are among the most common windy landscapes in Pashoueyeh Erg in the west of Lut Desert, Iran. This study aims to elaborate on morphological properties of barchans in this region using mathematical and statistical models. The results of these methods are very important in investigating barchan shapes and identifying their behavior. Barchan shapes were mathematically modeled by simulating them in the coordinate system through nonlinear parabolic equations, so that two separate equations were calculated for barchan windward and slip-face parabolas. The type and intensity of relationships between barchan morphology and mathematical parameters were determined by the statistical modeling. The results indicated that the existing relationships followed the power correlation with the maximum coefficient of determination and minimum error of estimate. Combining the above two methods is a powerful basis for stimulating barchans in virtual and laboratory environments. The most important result of this study is to convert the mathematical and statistical models of barchan morphology to each other. Focal length is one of the most important parameters of barchan parabolas, suggesting different states of barchans in comparison with each other. As the barchan's focal length decreases, its opening becomes narrower, and the divergence of the barchan's horns reduces. Barchans with longer focal length have greater width, dimensions, and volume. In general, identifying and estimating the morphometric and planar parameters of barchans is effective in how they move, how much they move, and how they behave in the environment. These cases play an important role in the management of desert areas.
This study evaluated the effectiveness of direct instruction flashcards with oral
Groundwater contamination risk mapping is one essential measure in groundwater management and quality control. The purpose of the present study is to address this mapping by means of a novel framework, which is more suitable for arid regions than other methods developed in previous work. Specifically, we integrate machine learning tools, interpolation and process-based models with a modified version of DRASTIC-AHP to evaluate groundwater vulnerability to nitrate contamination and to map this contamination in Jiroft plain, Iran. The DRASTIC model provides a tool for evaluating aquifer vulnerability by using seven parameters related to the hydrogeological setting (Depth to water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone, and hydraulic Conductivity), while the criteria ratings and weights of these parameters are evaluated by means of an Analytic Hierarchy Process (AHP). However, to obtain the risk map, the results about groundwater vulnerability are combined here with a contamination hazard map, which we estimate by applying ensemble modeling based, in part, on the occurrence probability predicted from Generalized Linear Model (GLM), Flexible Discriminant Analysis (FDA), and Support Vector Machine (SVM). Our integrated modeling framework provides an assessment of both regional patterns of groundwater contamination and an estimate of the impacts of the contamination based on socio-environmental variables, and is particularly suitable for applications based on limited amount of available data. The groundwater contamination risk map obtained from our case study shows that the central and southern regions of the Jiroft plain display high and very high contamination risk, which is associated with high production rate of urban waste in residential lands and an overuse of nitrogen fertilizers in agricultural lands. Therefore, our work is providing new modeling insights for the future assessment of groundwater contamination, with potential impacts for the management and control of water resources in arid and semi-arid environments.
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