The identification of potential sites for water harvesting is an important step towards maximizing water availability and land productivity in the arid and semi-arid areas. This research aimed to select the optimum sites for water harvesting in the Azraq basin of Jordan through the use of GIS techniques. The Azraq basin is characterized by flash floods that involve large quantities of runoff. The selection criteria in this research were based on six parameters identified based on an extensive literature review. Five experts were then asked to evaluate the importance of each criterion. The consistency ratio between the experts opinions was evaluated using the pairwise comparison method and a final weight was computed for each criterion. A water harvesting suitability map was then generated following the weighted linear combination (WLC) method. The sites that are not suitable for water harvesting within the study area were identified and eliminated following the Boolean method, and final water harvesting suitability map was generated. Finally, the findings of this research can be used to assist in the efficient planning of the water resources management to ensure a sustainable development of the water in Jordan and in other areas suffering from water shortages.

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Floods are one of the most destructive natural disasters causing financial damages and casualties every year worldwide. Recently, the combination of data‐driven techniques with remote sensing (RS) and geographical information systems (GIS) has been widely used by researchers for flood susceptibility mapping. This study presents a novel hybrid model combining the multilayer perceptron (MLP) and autoencoder models to produce the susceptibility maps for two study areas located in Iran and India. For two cases, nine, and twelve factors were considered as the predictor variables for flood susceptibility mapping, respectively. The prediction capability of the proposed hybrid model was compared with that of the traditional MLP model through the area under the receiver operating characteristic (AUROC) criterion. The AUROC curve for the MLP 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 Iran and India cases, respectively. The results suggested that the hybrid autoencoder‐MLP model outperformed the MLP model and, therefore, can be used as a powerful model in other studies for flood susceptibility mapping.

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The aim of this study was to identify potential sites for wind turbine in the North West of Jordan. The Analytic Hierarchy Process (AHP) was used in a novel approach to identify the potential sites for the wind turbine in the study area based on five physical criteria (Wind Speed, Rainfall, Slope, Altitude and Land use) that affect the wind turbine sites. The importance of each criterion was based on experts' opinions. The ratings for each criterion were based on the available literature review. The consistency ratio between the experts' opinions was evaluated using the pairwise comparison method and a final weight was computed for each criterion. A wind turbine suitability map was generated using the weighted linear combination (WLC) method within GIS environment. It was found that 45% of the study area has high and very high suitability for wind turbine. In conclusion, this research will contribute to the enhancement of the available renewable energy resources in Jordan if the selected sites will be utilized for wind turbine.