Short-term and long-term streamflow prediction by using 'wavelet–gene expression' programming approach

Abstract

Forecasting short-term and long-term streamflows is of most importance for analyzing water resources systems as well as for many other hydrological applications. In this study, a new combination method (wavelet–genetic programming) was proposed for predicting short-term and long-term streamflows. The new combination method combines the discrete wavelet transform and genetic programming methods to forecast streamflow. The results obtained showed very high agreement between the observed and modeled values. The combination models are compared with the auto regressive moving average and the classic artificial intelligences methodologies, including neuro-fuzzy system and neural networks. The benchmark results show that the new wavelet–genetic programming methodology surpasses all of the other applied models for predicting short-term and long-term streamflows.

Keywords:

• Streamflow
• wavelet transform
• genetic programming
• neuro-fuzzy
• neural networks

Disclosure statement

No potential conflict of interest was reported by the authors.