气象学报2024，Vol.82Issue(2)：257-273,17.DOI:10.11676/qxxb2024.20230075
基于数据驱动的山区暴雨山洪水沙灾害易发区早期识别方法研究
Early recognition of the mountainous areas susceptible to flash flood and sediment disasters during rainstorms:Data-driven methods
摘要
Abstract
In the field of flash flood disaster prevention study,great attention has been paid on the role of heavy rainfall and flooding,yet the coupling of flooding and sediment caused by silt deposition is largely neglected.This study revises the impact factor system by considering spatial heterogeneity of loose materials deposit,performs sensitivity analysis on complex underlying surface environment in mountainous watersheds,and utilizes the spatial analysis in geographic information systems and multicollinearity test to calculate the contribution indexes of various impact factors.A method for early identification of flash flood disaster is constructed by coupling different types of contribution-integrated learning algorithms,which is applied to identify the proneness to flash flood disaster in 5250 small watersheds in Aba prefecture.The results show that flash flood disasters exhibit certain aggregation in space,and the impact factors within specific ranges are more sensitive to disaster occurrence.Some impact factors share similar sensitivity patterns towards disaster occurrence.The eastern and central-southern areas and a small part of the northwestern area of Aba prefecture are highly prone areas,which are relatively close to the high-frequency area of solid material source,where relatively larger probability of flooding-sediment coupling disaster can be found.Lower prone areas are primarily distributed in the western and southwestern regions of Aba prefecture,where the overlap with the high-frequency area of solid matter source is relatively small and the flood tends to play a dominant role in the disaster process.Compared with the results of flash flood risk survey and assessment,the disaster density in high-prone area derived from results of data-driven early identification method is larger,and the high-risk coverage is increased by 23.2-45.4 percent.关键词
山洪水沙/易发性/影响因子/早期识别/集成学习Key words
Flash flood sediment disaster/Susceptibility/Impact factor/Early recognition/Ensemble learning分类
天文与地球科学引用本文复制引用
刘海知,徐辉,包红军,宋巧云,鲁恒,闫旭峰,狄靖月,杨寅..基于数据驱动的山区暴雨山洪水沙灾害易发区早期识别方法研究[J].气象学报,2024,82(2):257-273,17.基金项目
国家自然科学基金气象联合基金项目(U234220088)、国家重点研发计划项目(2019YFC1510702)、高原与盆地暴雨旱涝灾害四川省重点实验室开放研究基金课题(SZKT202308)、中国气象局创新发展专项(CXFZ2022J019). （U234220088）
