Reliable multi-station precipitation forecasting is challenging due to nonstationarity, noise, and spatial heterogeneity. This paper introduces a hybrid signal decomposition-machine learning benchmarking framework that integrates four decomposition methods (TQWT, MODWT, EWT, VMD) with three learners (Bagging, LSBoost, KNN), yielding twelve hybrid models. These models were rigorously tested across twelve stations in the Kébir R...

The modeling of rainfall ( P i ) and runoff ( Q i ) represents a significant challenge currently facing the field of hydrology. Numerous methodologies can be employed in this regard, spanning the spectrum from conceptual approaches to those that are entirely data-driven and physically based. This paper presents a method for estimating rainfall values at nine observation stations in the Tigris River Basin using four machine lea...