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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 Self-Organizing-Map (SOM)-based clustering technique was used to identify spatially homogeneous clusters of groundwater level (GWL) data for a feed-forward neural network (FFNN) to model one and multi-step-ahead GWLs. The wavelet transform (WT) was also used to extract dynamic and multi-scale features of the non-stationary GWL, runoff and rainfall time series. The performance of the FFNN model was compared to the newly proposed combined WT–FFNN model and also the conventional linear forecasting method of ARIMAX (Auto Regressive Integrated Moving Average with exogenous input). GWL predictions were investigated under three different scenarios.

The results indicated that the proposed FFNN model coupled with the SOM …