Wavelet-entropy data pre-processing approach for ANN-based groundwater level modeling

摘要

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.