Improved daily streamflow forecasting for semi-arid environments using hybrid machine learning and multi-scale analysis techniques

ABSTRACT This study aimed to improve daily streamflow forecasting by combining machine learning (ML) models with signal decomposition techniques. Four ML models were hybridized with five families of maximum overlap discrete wavelet transforms (MODWTs). The hybrid models were applied to predict daily streamflow at the Bir Ouled Taher station in northern Algeria. Model performance was evaluated using multiple statistical metrics and compared to standalone ML models. The hybrid MODWT-Gaussian process regression (GPR) model using Symlet wavelets (MODWT-GPR3 sym4) achieved the best performance, with R = 0.99 and NSE = 0.98 during validation. This significantly outperformed the standalone models tested and other hybrid combinations. The MODWT-GPR3 sym4 model demonstrated a superior ability to capture nonlinearities and predict peak flows. Hybridization of ML models with wavelet transforms, particularly the MODWT-GPR approach, can substantially improve daily streamflow prediction accuracy compared to standalone models. However, model performance may vary between watersheds due to differences in hydrological characteristics. Consideration of catchment concentration time when selecting model inputs could further enhance forecasting capabilities.