Flash Flood Regionalization for the Hengduan Mountains Region, China, Combining GNN and SHAP Methods
department or school: Arts, Faculty of
department or school: Non UBC
department or school: Geography, Department of
data provider: DSpace
identifier citation: Remote Sensing 17 (6): 946 (2025)
creator: Li, Yifan
creator: Zhang, Chendi
creator: Cui, Peng
creator: Hassan, Marwan A.
creator: Duan, Zhongjie
creator: Bhattacharyya, Suman
creator: Yao, Shunyu
creator: Zhao, Yang
issued: 2025-05-02T20:31:57Z
issued: 2025-03-07
description: The Hengduan Mountains region (HMR) is vulnerable to flash flood disasters, which account for the largest proportion of flood-related fatalities in China. Flash flood regionalization, which divides a region into homogeneous subdivisions based on flash flood-inducing factors, provides insights for the spatial distribution patterns of flash flood risk, especially in ungauged areas. However, existing methods for flash flood regionalization have not fully reflected the spatial topology structure of the inputted geographical data. To address this issue, this study proposed a novel framework combining a state-of-the-art unsupervised Graph Neural Network (GNN) method, Dink-Net, and Shapley Additive exPlanations (SHAP) for flash flood regionalization in the HMR. A comprehensive dataset of flash flood inducing factors was first established, covering geomorphology, climate, meteorology, hydrology, and surface conditions. The performances of two classic machine learning methods (K-means and Self-organizing feature map) and three GNN methods (Deep Graph Infomax (DGI), Deep Modularity Networks (DMoN), and Dilation shrink Network (Dink-Net)) were compared for flash-flood regionalization, and the Dink-Net model outperformed the others. The SHAP model was then applied to quantify the impact of all the inducing factors on the regionalization results by Dink-Net. The newly developed framework captured the spatial interactions of the inducing factors and characterized the spatial distribution patterns of the factors. The unsupervised Dink-Net model allowed the framework to be independent from historical flash flood data, which would facilitate its application in ungauged mountainous areas. The impact analysis highlights the significant positive influence of extreme rainfall on flash floods across the entire HMR. The pronounced positive impact of soil moisture and saturated hydraulic conductivity in the areas with a concentration of historical flash flood events, together with the posi…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/90985
note: Academic Editors: Ying Zhang,Peng Dou and Chunlin HuangReceived: 20 January 2025Revised: 19 February 2025Accepted: 5 March 2025Published: 7 March 2025Citation: Li, Y.; Zhang, C.; Cui, P.;Hassan, M.; Duan, Z.; Bhattacharyya,S.; Yao, S.; Zhao, Y. Flash FloodRegionalization for the HengduanMountains Region, China, CombiningGNN and SHAP Methods. RemoteSens. 2025, 17, 946. https://doi.org/10.3390/rs17060946Copyright: © 2025 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license(https://creativecommons.org/licenses/by/4.0/).ArticleFlash Flood Regionalization for the Hengduan MountainsRegion, China, Combining GNN and SHAP MethodsYifan Li 1,2 , Chendi Zhang 1,* , Peng Cui 1,2,3, Marwan Hassan 4 , Zhongjie Duan 5, Suman Bhattacharyya 4,Shunyu Yao 6 and Yang Zhao 71 Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and NaturalResources Research, Chinese Academy of Sciences, Beijing 100101, China; liyf.20b@igsnrr.ac.cn (Y.L.)2 University of Chinese Academy of Sciences, Beijing 100049, China3 China-Pakistan Joint Research Center on Earth Sciences, CAS-HEC, Islamabad 45320, Pakistan4 Department of Geography, University of British Columbia, Vancouver, BC V6T1Z2, Canada5 Department of Data Science and Engineering, East China Normal University, Shanghai 200062, China6 China Institute of Water Resources and Hydropower Research, Beijing 100038, China7 Sichuan Zipingpu Development Co., Ltd., Chengdu 610091, China* Correspondence: zhangchendi@igsnrr.ac.cnAbstract: The Hengduan Mountains region (HMR) is vulnerable to flash flood disasters,which account for the largest proportion of flood-related fatalities in China. Flash floodregionalization, which divides a region into homogeneous subdivisions based on flashflood-inducing factors, provides insights for the spatial distribution patterns of flash floodrisk, especially in ungau…
has type: Article
is shown at: 10.14288/1.0448731
language: eng
peer review status: Reviewed
provider: Vancouver : University of British Columbia Library
publisher: Multidisciplinary Digital Publishing Institute
publisher DOI: 10.3390/rs17060946
rights: CC BY 4.0
rights URI: https://creativecommons.org/licenses/by/4.0/
scholar level: Faculty
scholar level: Researcher
subject: flash flood
subject: Graph Neural Network (GNN)
subject: regionalization
subject: the Hengduan Mountains region (HMR)
subject: interpretation model
type: Text
identifier URI: http://hdl.handle.net/2429/90985