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.

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.

Simulation of dam breach scenarios can help in the preparation of emergency action plans for real dam breaks or flash flooding events. The purpose of this study was to identify flood-prone areas in the Al Wala Valley in the governorate of Madaba in Jordan through analysis of the Al Wala Dam. Modelling of dam breaches was conducted under two scenarios: a Clear Day scenario and a Probable Maximum Flood (PMF) scenario. The former scenario does not address the various dam failure modes; rather, it addresses the formation and development of a breach as a result of structural failures like the sliding of dam blocks in the case of a concrete dam or piping failures in the case of embankment dams. The PMF scenarios, however, simulate unsteady flow in pipes and overtopping failure via consideration of runoff hydrography. In the PMF scenario, flood-prone areas can be identified by in-depth analysis of data from previous extreme rainfall events. The related hydrologic and hydraulic data can then be modelled using intensity-duration-frequency curves applied to an hour-by-hour simulation to discover the areas most at risk of flooding in the future. In the present study, data were collected from inlet of flow to Al Wala Valley on 10 January 2013. The collected data, which included rainfall and discharge data, were fed to the HEC-HMS software in order to calibrate the hydrological parameters of the watershed of the Al Wala Dam. Additionally, the HEC-RAS tool was employed to determine the breach outflow hydrography and hydraulic conditions across various critical downstream locations, which were determined by use of dynamic flood wave-routing models. The simulations revealed that, in the case of the Clear Day scenario, downstream inundation would cover an area of 5.262 km² in the event of a pipe failure. However, in the event of a six-hour storm, a twelve-hour storm, and a twenty-four-hour storm, the flooded area would rise to 6.837 km², 8.518 km², and 9.390 km², respectively. In the event of an overtopping failure, 13.171 km² would be inundated, according to the Clear Day scenario. On the other hand, in the event of a six-hour storm, a twelve-hour storm, and a twenty four-hour storm, the flooded area would rise to 13.302 km², 14.249 km², and 14.594 km², respectively.

The groundwater contained in aquifers is among the most important water supply resources, especially in semi-arid and arid regions worldwide. This study aims to evaluate and compare the prediction capability of two well–known models, support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS), combined with a genetic algorithm (GA), invasive weed optimization (IWO), and teaching–learning-based optimization (TLBO) algorithms in groundwater potential mapping (GPM) the Azraq Basin in Jordan. The hybridization of the SVM and ANFIS models with the GA, IWO, and TLBO algorithms results in six models: SVM–GA, SVM–IWO, SVM–TLBO, ANFIS–GA, ANFIS–IWO, and ANFIS–TLBO. A database consisting of well data containing 464 wells with 12 predictive factors was developed for the groundwater potential mapping (GPM) of the study area. Of the 464 well locations, 70% (325 locations) were assigned for the training set and the rest (139 locations) for the validation set. The correlation between the 12 predictive factors and the well locations is analyzed using the frequency ratio (FR) statistical model. An area under receiver operating characteristic (AUROC) curve was used to evaluate and compare the models. According to the results, the SVM-based hybrid models outperformed other ANFIS hybrid models in the learning (training) and validation phases. The SVM–GA and SVM–TLBO hybrid models showed AUROC values of 0.984 and 0.971, respectively, in the training and validation phases. Moreover, the ANFIS–GA and ANFIS–TLBO hybrid models showed an AUROC of 0.979 and 0.984 in the training phase and an AUROC of 0.973 and 0.984 in the validation phase, respectively. The SVM–IWO and ANFIS–IWO hybrid models showed the lowest AUROC. This study demonstrated the more efficient results of the SVM-based hybrid models in comparison with the ANFIS-based hybrid models in terms of accuracy and modeling speed.

This study aims to investigate existing water management problems and potential solutions in the Al-Mafraq Governorate of Jordan, and to examine public awareness of water demand management at the household level. A questionnaire survey captured residents' perceptions of water quantity and quality. Results showed that 71% of respondents find the water pumped from the public system to be insufficient, and 40.9% find that water laws and regulations are implemented ineffectively. The study also showed that 85% of respondents are dependent upon groundwater wells, and 42.5% of respondents have suffered water contamination. Water quality is degraded due to pollution caused by solid waste and liquids, and salinity is increased due to the over-pumping and depletion of groundwater resources. The study recommends that the government incentivize residents to harvest water to deal with water shortages, religious motivations be explored in water conservation efforts, and water authorities should repair or replace old pipelines.