Prediction of groundwater flow in shallow aquifers using artificial neural networks in the northern basins of Algeria

Abstract

Prediction of groundwater flow fluctuations is considered an important step in understanding groundwater systems at this scale and facilitating sustainable groundwater management. The objective of this study is to determine the factors that influence and control groundwater flow fluctuations in a specific geomorphologic situation, by developing a forecasting model and examining its potential for predicting groundwater flow using limited data. Models for prediction of groundwater flow are developed based on artificial neural networks (ANNs). Neural networks with different numbers of hidden layer neurons were developed using climatic and geomorphological characteristics as input variables, giving predicted groundwater flow as the output. To evaluate enhanced performance models, several regression statistical parameters are compared. As an example, relative mean square error in groundwater flow prediction by ANN and correlation coefficient are 0.015 and 97%, respectively. The results of the study clearly show that ANNs can be used to predict groundwater flow in shallow aquifers of northern Algeria with reasonable accuracy even in the case of limited data. HIGHLIGHTS Combine hydrological and climatic data to estimate groundwater flows.; Test the performance of ANN's models to understand the behavior of groundwater.; Large-scale groundwater flow modeling for better management of water resources.; Proposal of a predictive model for a global vision of the distribution of groundwater.; Determining the order of importance of indicators that can influence groundwater flows.;

Keywords

• groundwater
• limited data
• model
• northern algeria
• prediction