A hybrid data-driven model integrating hydrometeorological factors for snowmelt flood early warning in an arid mountain basinOA
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.
WANG Zerui;LI Xiaoyang;LIU Yongqiang;WANG Weiping;LI Yaqian;ZHANG Yuanwei
College of Geography and Remote Sensing Sciences,Xinjiang University,Urumqi 830017,China Xinjiang Key Laboratory of Oasis Ecology,Xinjiang University,Urumqi 830017,ChinaCollege of Geography and Remote Sensing Sciences,Xinjiang University,Urumqi 830017,China Xinjiang Key Laboratory of Oasis Ecology,Xinjiang University,Urumqi 830017,ChinaCollege of Geography and Remote Sensing Sciences,Xinjiang University,Urumqi 830017,China Xinjiang Key Laboratory of Oasis Ecology,Xinjiang University,Urumqi 830017,ChinaCollege of Geography and Remote Sensing Sciences,Xinjiang University,Urumqi 830017,China Xinjiang Key Laboratory of Oasis Ecology,Xinjiang University,Urumqi 830017,ChinaCollege of Geography and Remote Sensing Sciences,Xinjiang University,Urumqi 830017,China Xinjiang Key Laboratory of Oasis Ecology,Xinjiang University,Urumqi 830017,ChinaCollege of Geography and Remote Sensing Sciences,Xinjiang University,Urumqi 830017,China Xinjiang Key Laboratory of Oasis Ecology,Xinjiang University,Urumqi 830017,China
天文与地球科学
snowmelt floodmultiple linear regression(MLR)backpropagation neural network(BPNN)simulated annealing(SA)Hutubi River Basin
《Journal of Arid Land》 2026 (8)
P.1331-1353,23
funded by the National Key Research and Development Program of China(2019YFC1510505)the National Undergraduate Training Program for Innovation and Entrepreneurship of Xinjiang University(202510755008).
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