Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/115028
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dc.contributorMainland Affairs Office-
dc.creatorHe, YF-
dc.creatorDing, MT-
dc.creatorDuan, Y-
dc.creatorZheng, H-
dc.creatorHe, W-
dc.creatorLiu, J-
dc.date.accessioned2025-09-02T00:32:18Z-
dc.date.available2025-09-02T00:32:18Z-
dc.identifier.issn1470-160X-
dc.identifier.urihttp://hdl.handle.net/10397/115028-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).en_US
dc.rightsThe following publication He, Y., Ding, M., Duan, Y., Zheng, H., He, W., & Liu, J. (2025). Debris flows dynamic risk assessment and interpretable Shapley method-based driving mechanisms exploring – A case study of the upper reach of the Min River. Ecological Indicators, 173, 113400 is available at https://dx.doi.org/10.1016/j.ecolind.2025.113400.en_US
dc.subjectDebris flowsen_US
dc.subjectRisk assessmenten_US
dc.subjectMachine learningen_US
dc.subjectModel interpretabilityen_US
dc.subjectIndicators contributionen_US
dc.titleDebris flows dynamic risk assessment and interpretable Shapley method-based driving mechanisms exploring - a case study of the upper reach of the Min Riveren_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume173-
dc.identifier.doi10.1016/j.ecolind.2025.113400-
dcterms.abstractDebris flow is one of the most devastating natural hazards. Identifying the dynamic changes and driving factors of debris flow risk can enhance hazard mitigation and prevention. It is not clear what factors can mostly lead to debris flow risk change in mountainous areas, particularly some of these areas in the context of intense earthquakes, rapid urbanization, and climate change. To address these questions, an ensemble learning model was constructed to estimate the debris flow risk of the baseline period (2000) and the current period (2020) in the upper reach of the Min River. The study found that the areas with extremely high debris flow risk decreased by 18.57%, while the areas with moderate and high risk levels increased by 8% and 14% respectively. With this trend of overall risk increasing, the population and buildings affected by extremely high debris flow risk have increased by 20% and 30% respectively. Based on the interpretable learning model of SHAP (The Shapley Additive Explanations value), the mechanisms by driven factors that lead to changes in risk were explored. Population, elevation and NDVI are the most influential factors leading to changes in risk. Specifically, the increase in risk in the low elevation area is due to the rapid urbanization caused by the increase of population and GDP. While the risk change in higher elevation areas contributes to the variation of vegetation and precipitation. These findings have implications for debris flow mitigation and contribute to the understanding of the multiple factors that impact debris flow risk.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEcological indicators, Apr. 2025, v. 173, 113400-
dcterms.isPartOfEcological indicators-
dcterms.issued2025-04-
dc.identifier.isiWOS:001460552900001-
dc.identifier.eissn1872-7034-
dc.identifier.artn113400-
dc.description.validate202509 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of China, Hazard-evolution mechanism of debris flow in different mountain vertical zones settlement areas in the upper reaches of Min River; Sichuan Provincial Science and Technology Department Program Funding, AI-based automatic early warning model and application demonstration of landslide disaster risk along Sichuan-Tibet Railway; Science and Technology Program of Aba City, R&D and Demonstration Application of Intelligent Identification and Dynamic Risk Prediction System for Geological Hazards on Mountain Slopes in West Sichuan Provinceen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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