The Selection of Groundwater Recharge Sites in the Arid Region of Northern Badia, Jordan, using GIS-Based Multicriteria Decision Analysis

DOI: 10.17014/ijog.5.3.199-209This study aims to locate new groundwater recharge sites in the arid region of northern Badia, Jordan, based on specific criteria including lithology, drainage and lineament density, soil texture, slope, and rainfall. With groundwater serving as the key source of Jordanian drinking water, the use of surface water for groundwater recharge is essential in maximizing the groundwater available. Groundwater recharge sites were selected using the weighted linear combination (WLC) method with the aforementioned criteria. According to the findings, 5.064% of the region is very highly suited to groundwater recharge, 33.599% of the region is highly suited, and 3.789% of the region is moderately suited. However, 26.634% of the region is poorly suited to groundwater recharge, with a further 30.943% being very poorly suited. The significance of each criterion for groundwater recharge was identified using removal analysis, with the most significant factor being efficient groundwater management. Given this finding, big data are required in order to determine the optimal locations for groundwater recharge as part of future groundwater planning and management.</div

The Use of Vector-Based GIS and Multi-Criteria Decision Making (MCDM) for Siting Water Harvesting Dams in Karak Governorate/ South Jordan

Jordan is the 4th poorest country in the world in terms of water resources. Although, Jordan receives an average annual rainfall of 8194 million cubic metre, it can only collect 360 million cubic meters in its existing dams. There is an urgent need to construct more dams in order to harvest the obtainable runoff which might help in overcoming the shortage in its water resources for domestic and agricultural uses.  Site selection of dams must be carried out using sophisticated tools and techniques. One of these techniques is GIS, which could be integrated with multi-criteria decision making (MCDM) to select the optimum sites for dams. In this research vector-Based GIS and multi-criteria decision making were used to select the optimum sites of dams in Karak governorate/ South Jordan. Rainfall, soil, slope, urban areas and roads comprise the selection criteria used in this research based on the use of weighted linear combination (WLC). Wadis, Roads, Urban Centres, Faults and Wells comprise the constraint factors used to erase the unsuitable areas for constructing dams based on the Boolean technique. The outcome of this research showed that there are 9 potential sites that could be utilized for constructing dams to harvest the surface water in the study area. Keywords: Jordan; Karak; Dams; Vector-Based; GIS; MCD

A modified analytical hierarchy process method to select sites for groundwater recharge in Jordan

The aim of this study was to identify potential sites for groundwater recharge in the Azraq basin in Jordan. Several research questions were answered in this study including how to utilize the views and opinions of multiple experts in the field of groundwater recharge within a spatial analysis framework to identify the suitable sites for groundwater recharge in the study area and check the consistency in these opinions and their spatial representation. The Analytic Hierarchy Process (AHP) was modified in a novel approach to identify the potential sites for the groundwater recharge in the study area. First, the physical criteria that affect the groundwater recharge were identified based on an extensive literature review. Seventeen experts were then asked to evaluate the importance of each criterion. The consistency ratio between the experts opinions were evaluated using the pairwise comparison method and a final weight was computed for each criterion. A groundwater recharge suitability map was then generated following the weighted linear combination (WLC) method. The sites that are not suitable for groundwater recharge within the study area were identified and eliminated following the Boolean method, and a final groundwater recharge suitability map was generated. The outcome of the GIS analysis of this study was evaluated against field investigations carried out in the study area. Time Domain Electromagnetic (TDEM) and Soil Texture Analysis were used on sixteen locations distributed in eight sites within the study area. The results acquired by the field investigation agreed well with the GIS acquired results. The knowledge generated by this analysis may provide information on potential recharge zones. Finally, the findings of this research can be used to assist in the efficient planning of the groundwater management to ensure a sustainable development of the groundwater in Jordan and in other areas suffering from water shortages

Integrating Indigenous Knowledge with MCDA in the GIS Environment to Determine Site Potential for Water Harvesting in Wadi Hammad Basin in Jordan

The significance of water harvesting in Wadi Hammad basin lies in the fact that the Jordanian government encourages the cultivation of vegetables, wheat, and barley in the country in an effort to improve food security in Jordan and create job opportunities for young people in the agricultural sector. Water harvesting in this basin will augment the water resources used for plant production and livestock watering by flash floods that involve large quantities of runoff. This study aimed to identify the best locations for water harvesting in the Wadi Hammad basin in Jordan via a Multi-Criterion Decision Analysis (MCDA) and indigenous knowledge. This study focused on consulting with indigenous knowledge where they provided information on the study area for water-collecting sites that have been used for years to provide water. In this study, site selection was based on six criteria that had been determined through a review of related literature (drainage density, rainfall depth, lineament density, soil clay content, geology, and slope). Following MCDA analysis, a water-harvesting suitability map was created. The final water-harvesting map uncovered that a large part of the basin (66.53%) has high to very high potential for water harvesting. The technique of water harvesting was subdued to statistical analysis, sensitivity analysis, and the map removal test. This study demonstrates that the selection of relevant water harvesting locations is a lengthy method that needs consultation with indigenous knowledge and the use of MCDA in the GIS environment. The study results, in general, and the final map, in particular, show the good relationship between the sites defined by the use of MCDA and the site suitability for water harvesting that was specified based on indigenous knowledge. Finally, the results of this study, which integrated indigenous knowledge with MCDA, may be employed to help in effective planning for water resource management to warrant the sustainable development of water in Jordan

Spatial mapping of water spring potential using four data mining models

Population growth and overexploitation of water resources pose ongoing pressure on groundwater resources. This study compares the capability of four data mining methods, namely, boosted regression tree (BRT), random forest (RF), multivariate adaptive regression spline (MARS), and support vector machine (SVM), for water spring potential mapping (WSPM) in Al Kark Governorate, east of the Dead Sea, Jordan. Overall, 200 spring locations and 13 predictor variables were considered for model building and validation. The four models were calibrated and trained on 70% of the spring locations (i.e., 140 locations) and their predictive accuracy was evaluated on the remaining 30% of the locations (i.e., 60 locations). The area under the receiver operating characteristic curve (AUROCC) was employed as the performance measure for the evaluation of the accuracy of the constructed models. Results of model accuracy assessment based on the AUROCC revealed that the performance of the RF model (AUROCC = 0.748) was better than that of any other model (AUROCC SVM = 0.732, AUROCC MARS = 0.727, and AUROCC BRT = 0.689). HIGHLIGHTS Groundwater potential zoning and mapping is a critical step in identifying and managing water resources.; Multicriteria decision-making methods can be employed as fast and efficient techniques in decision-making.; The possibility of the presence of multicollinearity among the 13 predictors of the presence of water springs was examined.

Integrating Indigenous Knowledge with MCDA in the GIS Environment to Determine Site Potential for Water Harvesting in Wadi Hammad Basin in Jordan

The significance of water harvesting in Wadi Hammad basin lies in the fact that the Jordanian government encourages the cultivation of vegetables, wheat, and barley in the country in an effort to improve food security in Jordan and create job opportunities for young people in the agricultural sector. Water harvesting in this basin will augment the water resources used for plant production and livestock watering by flash floods that involve large quantities of runoff. This study aimed to identify the best locations for water harvesting in the Wadi Hammad basin in Jordan via a Multi-Criterion Decision Analysis (MCDA) and indigenous knowledge. This study focused on consulting with indigenous knowledge where they provided information on the study area for water-collecting sites that have been used for years to provide water. In this study, site selection was based on six criteria that had been determined through a review of related literature (drainage density, rainfall depth, lineament density, soil clay content, geology, and slope). Following MCDA analysis, a water-harvesting suitability map was created. The final water-harvesting map uncovered that a large part of the basin (66.53%) has high to very high potential for water harvesting. The technique of water harvesting was subdued to statistical analysis, sensitivity analysis, and the map removal test. This study demonstrates that the selection of relevant water harvesting locations is a lengthy method that needs consultation with indigenous knowledge and the use of MCDA in the GIS environment. The study results, in general, and the final map, in particular, show the good relationship between the sites defined by the use of MCDA and the site suitability for water harvesting that was specified based on indigenous knowledge. Finally, the results of this study, which integrated indigenous knowledge with MCDA, may be employed to help in effective planning for water resource management to warrant the sustainable development of water in Jordan

Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression

In this study, groundwater springs potentiality maps were prepared using a novel integrated model, support vector regression (SVR) with genetic algorithm (GA), for the Jerash and Ajloun region, Jordan.The conditioning factors such as altitude, aspect, slope angle, plan curvature, stream power index, topographic wetness index, length of slope, distance from drainage network, lithology, distance from faults, land use and normalised difference vegetation index were considered to map the groundwater spring potentiality. GA was used for two purposes. First, GA was used to optimize the hyper-parameters of the radial basis function (RBF) kernel of SVR model. Second, GA in combination with SVR was used as feature selection (FS).The results of these models were compared with common grid search (GS) method used in most of the studies. The GS method was employed to calculate the parameters related to the SVR model and also hyper-parameters of RBF kernel. The results show optimum values of the kernels in the SVR model and selecting the optimal features which have the most contribution in modeling were the major steps in modeling and also in achieving a desirable precision level

Novel hybrid models combining meta-heuristic algorithms with support vector regression (SVR) for groundwater potential mapping

This study aims to develop three novel GIS-based models combining Genetic Algorithm (GA), Biogeography-Based Optimization (BBO) and Simulated Annealing (SA) with Support Vector Regression (SVR) for groundwater potential (GP) mapping in the governorate of Tafillah, Jordan. Twelve topographical, hydrological and geological factors were considered. The mapping process was done with and without feature selection (FS) conducted by integration of SVR model with GA, BBO and SA algorithms. The accuracy of these models was evaluated using the area under receiver operating characteristic (AUROC) curve. Comparisons among the models uncovered that the SVR-RBF-GA and SVR-RBF-BBO models performed better than the SVR-RBF-SA. The AUROC for two mentioned models were 0.964 and 0.996 in training and testing runs, respectively, while this metric was 0.953 and 0.986 for SVR-RBF-SA model in training and testing runs, respectively. The results showed that after FS, the models are more accurate in test data than train data

Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks

Floods are one of the most destructive natural disasters causing financial dam-ages and casualties every year worldwide. Recently, the combination of data-driven techniques with remote sensing (RS) and geographical information sys-tems (GIS) has been widely used by researchers for flood susceptibility map-ping. This study presents a novel hybrid model combining the multilayerperceptron (MLP) and autoencoder models to produce the susceptibility mapsfor two study areas located in Iran and India. For two cases, nine, and twelvefactors were considered as the predictor variables for flood susceptibility map-ping, respectively. The prediction capability of the proposed hybrid model wascompared with that of the traditional MLP model through the area under thereceiver operating characteristic (AUROC) criterion. The AUROC curve for theMLP and autoencoder-MLP models were, respectively, 75 and 90, 74 and 93%in the training phase and 60 and 91, 81 and 97% in the testing phase, for Iranand India cases, respectively. The results suggested that the hybridautoencoder-MLP model outperformed the MLP model and, therefore, can beused as a powerful model in other studies for flood susceptibility mapping.Validerad;2021;Nivå 2;2021-02-22 (johcin)</p