Long-term monthly streamflow forecasting in humid and semiarid regions

Abstract

Long-term monthly streamflow forecasting has great importance in the water resource system planning. However, its modelling in extreme cases is difficult, especially in semiarid regions. The main purpose of this paper is to evaluate the accuracy of artificial neural networks (ANNs) and hybrid wavelet-artificial neural networks (WA-ANNs) for multi-step monthly streamflow forecasting in two different hydro-climatic regions in Northern Algeria. Different issues have been addressed, both those related to the model’s structure and those related to wavelet transform. The discrete wavelet transform has been used for the preprocessing of the input variables of the hybrid models, and the multi-step streamflow forecast was carried out by means of a recursive approach. The study demonstrated that WA-ANN models outperform the single ANN models for the two hydro-climatic regions. According to the performance criteria used, the results highlighted the ability of WA-ANN models with lagged streamflows, precipitations and evapotranspirations to forecast up to 19 months for the humid region with good accuracy [Nash–Sutcliffe criterion (Ns) equal 0.63], whereas, for the semiarid region, the introduction of evapotranspirations does not improve the model’s accuracy for long lead time (Ns less than 0.6 for all combinations used). The maximum lead time achieved, for the semiarid region, was about 13 months, with only lagged streamflows as inputs.