A novel flood conditioning factor based on topography for flood susceptibility modeling  

作　　者：Jun Liu Xueqiang Zhao Yangbo Chen Huaizhang Sun Yu Gu Shichao Xu 

机构地区：[1]School of Geography and Planning,Sun Yat-sen University,Guangzhou 510275,China

出　　处：《Geoscience Frontiers》2025年第1期209-222,共14页地学前缘(英文版)

基　　金：supported by National Natural Science Foundation of China(Grant No.U2243227);the Science and Technology Program of Guangdong Province(No.2020B1515120079);National Flash Flood Investigation and Evaluation Project(Grant No.SHZH-IWHR-57).

摘　　要：Flood is one of the most devastating natural hazards.Employing machine learning models to construct flood susceptibility maps has become a pivotal step for decision-makers in disaster prevention and management.Existing flood conditioning factors inadequately account for regional characteristics of flood in the depiction of topography,potentially leading to an overestimation of flood susceptibility in flat areas.Addressing this gap,this study proposes a novel flood conditioning factor,local convexity factor(LCF),to enhance the accuracy of flood susceptibility modeling.Initially,LCF is computed based on a standard normal Gaussian surface to highlight elevation variations in local terrain.Subsequently,LCF is applied to flood susceptibility modeling using seven machine learning models across four distinct basins.Comparative analysis is conducted between flood susceptibility maps with and without the application of LCF to evaluate its impact on flood susceptibility modeling.The results demonstrate that the proposed LCF can enhance the accuracy of flood susceptibility modeling to varying degrees,across the four basins investigated.The Fujiang basin exhibited the most substantial improvement,with its AUC improved from 0.861 to 0.886,Producer’s Agreement improved from 0.869 to 0.899,and Overall Agreement improved from 0.778 to 0.811.Comparation with hydrodynamic inundation maps shows that particularly in relatively flat terrain areas,flood susceptibility maps incorporating LCF offer more precise delineation between flood-prone and non-flood-prone zones.This research holds potential for widespread application in the prediction of flood susceptibility using machine learning models,providing a novel perspective for enhancing their accuracy.

关 键 词：Flood susceptibility prediction Flood conditioning factor Machine learning model Local terrain features 

分 类 号：P33[天文地球—水文科学]