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|Title||Modeling of Groundwater Level in Coastal Aquifers Using Artificial Neural Networks – Gaza Costal Aquifer as Case Study|
|Title in Arabic||Modeling of Groundwater Level in Coastal Aquifers Using Artificial Neural Networks – Gaza Costal Aquifer as Case Study|
Gaza coastal aquifer (GCA) is the most precious natural source in Gaza Strip where it is the only source of water for different uses. The extraction of groundwater currently exceeds the aquifer recharge rate; as a result, the groundwater level (GWL) is falling continuously. Therefore, forecasting of GWL is one of the most important requirements for effective management of groundwater. The undertaken research is concerned with the development of GWL model using Artificial Neural Networks (ANNs). The applicability of the developed ANNs models in simulating GWL was investigated for a part GCA. In this study, dependent variable used in the developed ANNs model were the initial GWL, recharge from different source such as rainfall, recharge from return flow from both water and wastewater networks systems, recharge from return flow from irrigation water, abstraction from both municipal wells and agricultural wells. The aforementioned input variables were used to predict GWL at 17 monitoring wells which distributed over all study area. The performance of the best ANNs model was satisfactory where the correlation between the observed and predicted values of GWL was 0.993. Moreover, ANNs model was utilized as a decision support tool by considering two future abstraction scenarios.
|Published in||The Fifth International Conference on Engineering and Sustainability (ICES5)|
|Publisher||The Islamic University of Gaza|
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