A wavelet neural network conjunction model for groundwater level forecasting

Accurate and reliable groundwater level forecasting models can help ensure the sustainable use of a watershed's aquifers for urban and rural water supply. In this paper, a new method based on coupling discrete wavelet transforms (WA) and artificial neural networks (ANN) for groundwater level forecasting applications is proposed. The relative performance of the proposed coupled wavelet-neural network models (WA-ANN) was compared to regular artificial neural network (ANN) models and autoregressive integrated moving average (ARIMA) models for monthly groundwater level forecasting. The variables used to develop and validate the models were monthly total precipitation, average temperature and average groundwater level data recorded from November 2002 to October 2009 at two sites in the Chateauguay watershed in Quebec, Canada. The WA-ANN models were found to provide more accurate monthly average groundwater level forecasts compared to the ANN and ARIMA models. The results of the study indicate the potential of WA-ANN models in forecasting groundwater levels. It is recommended that additional studies explore this proposed method, which can be used in turn to facilitate the development and implementation of more effective and sustainable groundwater management strategies. (C) 2011 Elsevier B.V. All rights reserved.