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The use of wavelet transformation in rainfall–runoff modeling has become popular because of its ability to simultaneously deal with both the spectral and the temporal information contained within time series data. The selection of an appropriate wavelet function plays a crucial role for successful implementation of the wavelet based rainfall–runoff artificial neural network models as it can lead to...
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...
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