Comparative study of the three models (ANN-PMC), (DWT-ANN-PMC) and (MLR) for prediction of the groundwater level of the surface water table in the Saïss Plain (North of Morocco)

Abstract

A new method based on the coupling of discrete wavelets (DWT) and artificial neural networks with perceptron multilayers (ANN-PMC) is proposed to predict the groundwater level. The relative performance of the DWT-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 DWT-ANN-PMC models in the prediction of groundwater levels. The forecast results indicate that the coupled wavelet neural network (WN) models were the best models for forecasting SPI values over multiple lead times in the Saïss Plain. 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.