Yuhang Zhu , Kunlong Yin , Ye Li , Haoran Yang , Hong Chen , Chao Zhou , Samuele Segoni
{"title":"利用人工智能量化坡面降雨敏感性,以细化区域滑坡降雨阈值","authors":"Yuhang Zhu , Kunlong Yin , Ye Li , Haoran Yang , Hong Chen , Chao Zhou , Samuele Segoni","doi":"10.1016/j.enggeo.2025.108260","DOIUrl":null,"url":null,"abstract":"<div><div>Regional-scale landslide early warning systems are commonly developed based on empirical rainfall thresholds. However, current rainfall threshold models overlook the significant spatial variations in slopes response to rainfall, thus undermining the accuracy of warnings. In this study, we improved a regional-scale landslide early warning method based on rainfall thresholds by accounting for the varying rainfall sensitivity of individual slope units. First, the Rainfall Sensitivity Index (RSI) for both dry and wet seasons are computed using a Graph Attention Network (GAN) model, incorporating ten influencing factors. Subsequently, an initial regional critical rainfall threshold was obtained based on the Effective cumulative rainfall – rainfall Duration (<em>E</em>-D). Finally, the improved rainfall thresholds for each slope units are derived by coupling the initial critical rainfall threshold and RSI. Using Pingyang County, Zhejiang Province, China as test site, the reliability and reasonableness of the proposed method was validated by statistical analysis, in-situ tests and historical rainfall events. The results reveal that the GAN demonstrates robust predictive capability in RSI assessment, achieving a precision of 0.88. Notably, slopes exhibit higher rainfall sensitivity during dry season compared to wet season. The early warning system employing improved critical thresholds shows significant improvement, with 11.9 % higher accuracy and 14.3 % fewer missed alarms relative to conventional methods. Overall, this study proposes a novel method for downscaling of regional-scale thresholds to slope-unit levels in landslide early warning systems, which informs risk mitigation strategies and governmental decision-making, thereby effectively reducing the risk of landslides.</div></div>","PeriodicalId":11567,"journal":{"name":"Engineering Geology","volume":"355 ","pages":"Article 108260"},"PeriodicalIF":8.4000,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Leveraging artificial intelligence to quantify slope rainfall sensitivity for refining regional landslide rainfall thresholds\",\"authors\":\"Yuhang Zhu , Kunlong Yin , Ye Li , Haoran Yang , Hong Chen , Chao Zhou , Samuele Segoni\",\"doi\":\"10.1016/j.enggeo.2025.108260\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Regional-scale landslide early warning systems are commonly developed based on empirical rainfall thresholds. However, current rainfall threshold models overlook the significant spatial variations in slopes response to rainfall, thus undermining the accuracy of warnings. In this study, we improved a regional-scale landslide early warning method based on rainfall thresholds by accounting for the varying rainfall sensitivity of individual slope units. First, the Rainfall Sensitivity Index (RSI) for both dry and wet seasons are computed using a Graph Attention Network (GAN) model, incorporating ten influencing factors. Subsequently, an initial regional critical rainfall threshold was obtained based on the Effective cumulative rainfall – rainfall Duration (<em>E</em>-D). Finally, the improved rainfall thresholds for each slope units are derived by coupling the initial critical rainfall threshold and RSI. Using Pingyang County, Zhejiang Province, China as test site, the reliability and reasonableness of the proposed method was validated by statistical analysis, in-situ tests and historical rainfall events. The results reveal that the GAN demonstrates robust predictive capability in RSI assessment, achieving a precision of 0.88. Notably, slopes exhibit higher rainfall sensitivity during dry season compared to wet season. The early warning system employing improved critical thresholds shows significant improvement, with 11.9 % higher accuracy and 14.3 % fewer missed alarms relative to conventional methods. Overall, this study proposes a novel method for downscaling of regional-scale thresholds to slope-unit levels in landslide early warning systems, which informs risk mitigation strategies and governmental decision-making, thereby effectively reducing the risk of landslides.</div></div>\",\"PeriodicalId\":11567,\"journal\":{\"name\":\"Engineering Geology\",\"volume\":\"355 \",\"pages\":\"Article 108260\"},\"PeriodicalIF\":8.4000,\"publicationDate\":\"2025-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Geology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0013795225003564\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, GEOLOGICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Geology","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0013795225003564","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
Leveraging artificial intelligence to quantify slope rainfall sensitivity for refining regional landslide rainfall thresholds
Regional-scale landslide early warning systems are commonly developed based on empirical rainfall thresholds. However, current rainfall threshold models overlook the significant spatial variations in slopes response to rainfall, thus undermining the accuracy of warnings. In this study, we improved a regional-scale landslide early warning method based on rainfall thresholds by accounting for the varying rainfall sensitivity of individual slope units. First, the Rainfall Sensitivity Index (RSI) for both dry and wet seasons are computed using a Graph Attention Network (GAN) model, incorporating ten influencing factors. Subsequently, an initial regional critical rainfall threshold was obtained based on the Effective cumulative rainfall – rainfall Duration (E-D). Finally, the improved rainfall thresholds for each slope units are derived by coupling the initial critical rainfall threshold and RSI. Using Pingyang County, Zhejiang Province, China as test site, the reliability and reasonableness of the proposed method was validated by statistical analysis, in-situ tests and historical rainfall events. The results reveal that the GAN demonstrates robust predictive capability in RSI assessment, achieving a precision of 0.88. Notably, slopes exhibit higher rainfall sensitivity during dry season compared to wet season. The early warning system employing improved critical thresholds shows significant improvement, with 11.9 % higher accuracy and 14.3 % fewer missed alarms relative to conventional methods. Overall, this study proposes a novel method for downscaling of regional-scale thresholds to slope-unit levels in landslide early warning systems, which informs risk mitigation strategies and governmental decision-making, thereby effectively reducing the risk of landslides.
期刊介绍:
Engineering Geology, an international interdisciplinary journal, serves as a bridge between earth sciences and engineering, focusing on geological and geotechnical engineering. It welcomes studies with relevance to engineering, environmental concerns, and safety, catering to engineering geologists with backgrounds in geology or civil/mining engineering. Topics include applied geomorphology, structural geology, geophysics, geochemistry, environmental geology, hydrogeology, land use planning, natural hazards, remote sensing, soil and rock mechanics, and applied geotechnical engineering. The journal provides a platform for research at the intersection of geology and engineering disciplines.