基于机器学习降雨动态时空特征识别山丘区小流域洪水预报方法研究OA北大核心CSTPCD

The mountainous region experiences fast-flowing and highly destructive floods,posing challenges for accurate and timely forecasting.Enhancing the accuracy and lead time of flood prediction in mountainous areas is a pressing issue.Addressing this concern,this paper proposes an innovative flood forecasting method based on ma-chine learning technology.The approach identifies historical rainfall-flood events with the most similarity to the current dynamic spatiotemporal features of rainfall,employing a"learn from the past to predict the present"strate-gy.The results indicate that,in small watersheds with minimal human influence and a basin area of approximately 600 km2 in mountainous regions,the method not only predicts the overall trend of rainfall but also forecasts the asso-ciated mountainous flood processes under this rainfall trend.The average errors for peak flow,flood volume,and peak time are 8.33％,14.27％,and 1 hour,respectively,meeting the accuracy requirements for flood forecasting.Distinguished from traditional flood forecasting methods,this approach predicts mountainous floods from the per-spective of the overall rainfall trend,providing a targeted strategy for flood forecasting in small watersheds in hilly areas.