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

Research on flood forecasting method in mountainous small watersheds based on machine learning for identifying rainfall dynamic spatiotemporal features

山丘区洪水产汇流速度快,破坏力强,预报难度大。如何进一步提高山丘区洪水预报的准确性和预见期,是当前亟待解决的主要问题。针对该问题,本文基于机器学习技术,创新性地提出了一种洪水预报的新方法。该方法通过识别与当前降雨动态时空特征最相似的历史降雨洪水过程,“借古喻今”进行洪水预报。结果表明,在人为影响小、流域面积在600 km^(2)左右的山丘区小流域,该方法预报洪峰流量平均误差为8.33%,洪量平均误差为14.27%,峰现时间平均误差1 h,均达到了洪水预报精度要求。区别于传统的洪水预报方法,该方法从整场降雨发展趋势的角度上预报山洪,更有针对性,为山丘区小流域洪水预报提供了新思路,为“三道防线”数据深度挖掘,防洪“四预”智能化水平提升提供有力技术支撑。

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 machine 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”strategy.The results indicate that,in small watersheds with minimal human influence and a basin area of approximately 600 km^(2)in mountainous regions,the method not only predicts the overall trend of rainfall but also forecasts the associated 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 perspective of the overall rainfall trend,providing a targeted strategy for flood forecasting in small watersheds in hilly areas.