River Stage Forecasting Using Wavelet Packet Decomposition and Data-driven Models

Abstract

This study develops and applies hybrid models combining wavelet packet decomposition and data-driven models for forecasting river stage and investigating their accuracy. The hybrid models are wavelet packet-based artificial neural network (WPANN) and wavelet packet-based adaptive neuro-fuzzy inference system (WPANFIS). Wavelet packet decomposition splits an input time series into approximation and detail components, and the decomposed time series are used as inputs to artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WPANN and WPANFIS models, respectively. The forecasting performances of WPANN and WPANFIS models are compared with those of ANN and ANFIS models. Based on performance evaluation indexes and graphical comparison, the WPANN and WPANFIS models produce better performance than ANN and ANFIS models in terms of model efficiency, and WPANFIS-coif18 model is also found to yield the best performance among all other models. Therefore, wavelet packet decomposition improves the accuracy of ANN and ANFIS models for forecasting river stage. The results indicate that river stage forecasting models combining wavelet packet decomposition and data-driven models can be used as an effective tool for forecasting river stage accuracy.