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Groundwater Level Predicted in the Saïss Plain (Northern Morocco) using Artificial Intelligence Techniques

Abdelhamid El ibrahimi, Abdennasser Baali

Abstract



This study describes the application of artificial intelligence techniques and Wavelet-Artificial Neural Network (SWT) models for the prediction of groundwater levels. The coupling of discrete wavelets (SWT) method and artificial neural networks with perceptron multilayers (ANN-PMC) is proposed. The relative performance of the SWT-ANN-PMC model has been regularly compared to artificial neural network (ANN-PMC) and multiple linear regression (MLR) models. Precipitation, temperature and average groundwater level are the variables introduced to explain and validate the models, with a monthly time step for the period March 1980 to March 2014 at two sites in the Plain of Saïss. The results of the study indicate the potential of SWT-ANN-PMC models in the prediction of groundwater levels. It is recommended that further studies should explore this proposed methodology, which may in turn be used to facilitate the development and implementation of more effective strategies for the sustainable management of groundwater

Keywords


artificial neural networks, ANN-PMC, SWT-ANN-PMC, groundwater level, MLR, the Plain of Saïss.

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