Research on flood forecasting method in mountainous small watersheds based on machine learning for identifying rainfall dynamic spatiotemporal features
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.
artificial intelligencemanifold learningspatiotemporal characteristics of rainfallflood forecasting in small watersheds of mountainous regions
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