Multi Resolution Genetic Programming Approach for Stream Flow Forecasting

Genetic Programming (GP) is increasingly used as an alternative for Artificial Neural Networks (ANN) in many applications viz, forecasting, classification etc. However, GP models are limited in scope as their application is restricted to stationary systems. This study proposes use of Multi Resolution Genetic Programming (MRGP) based approach as an alternative modelling strategy to treat non-stationaries. The proposed approach is a synthesis of Wavelets based Multi-Resolution Decomposition and Genetic Programming. Wavelet transform is used to decompose the time series at different scales of resolution so that the underlying temporal structures of the original time series become more tractable. Further, Genetic Programming is then applied to capture the underlying process through evolutionary algorithms. In the case study investigated, the MRGP is applied for forecasting one month ahead stream flow in Fraser River, Canada, and its performance compared with the conventional, but scale insensitive, GP model. The results show the MRGP as a promising approach for flow forecasting.