Forecasting of groundwater level fluctuations using a hybrid of multi-discrete wavelet transforms with artificial intelligence models

Abstract

Abstract Groundwater is often one of the significant natural sources of freshwater supply, especially in arid and semi-arid regions, and is of paramount importance. This study provides a new and high accurate technique for forecasting groundwater level (GWL). The artificial intelligence (AI) models include the artificial neural network (ANN) of multi-layer perceptron (MLP) and radial basis function network (RBF), and adaptive neural-fuzzy inference system (ANFIS) models. Input data to the models is the monthly average GWL of 17 piezometers. In this study, a preprocessing of data including the discrete wavelet transform (DWT) and multi-discrete wavelet transform (M-DWT) simultaneously was utilized. The results showed that the hybrid M-DWT-ANN, M-DWT-RBF, and M-DWT-ANFIS models compared to the DWT-ANN, DWT-RBF, and DWT-ANFIS models as well as than regular ANN, RBF, and ANFIS models, had the highest accuracy in forecasting GWL for the 1-, 2-, 3-, and 6-months ahead. Also, the M-DWT-ANN model had the best performance. Overall, the results illustrated that using the M-DWT method as preprocessing of input data can be a valuable tool to increase the predictive model's accuracy and efficiency. The results of this study indicate the potential of M-DWT-AI hybrid models to improve GWL forecasting.