A hybrid data-driven model integrating hydro- meteorological factors for snowmelt flood early warning in an arid mountain basin

High false alarm rates (FARs) in snowmelt flood forecasting persist, largely due to an insufficient understanding of the coupled effects of multi-source hydro-meteorological drivers and their inherent time lags. This study addressed this gap by developing a hybrid snowmelt-flood forecasting framework that combined multiple linear regression (MLR) and backpropagation neural network (BPNN) models, with a simulated annealing (SA) algorithm employed to optimize the ensemble weighting. The developed hybrid model that incorporated daily hydro-meteorological inputs and temporal factor was validated using data from 1978 to 2011 for the Hutubi River Basin, an arid inland basin on the northern slope of the Tianshan Mountains, China. The findings demonstrated that the hybrid model achieved a specificity of 0.8549, significantly outperforming standalone MLR (0.6024) and BPNN (0.6436) models. Correspondingly, the FAR was reduced to 0.1451, which was substantially lower than that of MLR (0.3976) and BPNN (0.3564). Upon integrating temporal factor, the FAR was further reduced to 0.0959, markedly enhancing overall predictive robustness. Collectively, this study offers a robust methodological framework for optimizing snowmelt flood forecasting by effectively integrating multi-source data and temporal dependencies.