Ho-Hong-Duy Nguyen, Ananta Man Singh Pradhan, Chang-Ho Song, Ji-Sung Lee, Yun-Tae Kim
{"title":"基于物理模型和极值分析的混合方法,用于分析降雨引发山体滑坡的时间概率","authors":"Ho-Hong-Duy Nguyen, Ananta Man Singh Pradhan, Chang-Ho Song, Ji-Sung Lee, Yun-Tae Kim","doi":"10.1007/s10346-024-02366-x","DOIUrl":null,"url":null,"abstract":"<p>The interplay between climate change–induced extreme rainfall and slope failure mechanisms presents a significant challenge. To address this, a new temporal modeling of landslides that integrates dynamic rainfall patterns with slope failure mechanisms is proposed. The approach features three steps: (1) analysis of the critical continuous rainfall (CCR) level using a physics-based model with Monte Carlo simulation; (2) calculation of the cumulative distribution function of the generalized extreme value distribution; and (3) estimation of the temporal probability map. Then, combined with the landslide spatial probability obtained from one-dimensional convolution neural network (1D-CNN), the landslide hazard probability was estimated for future periods of 5, 10, 20, and 50 years. The CCR and spatial probability maps were validated using the 2018 landslide event in Hiroshima Prefecture, Japan. The CCR map achieves an area under the receiver operating curve (AUC) of 74.8%. Cohesion and friction angle are the most sensitive in the hybrid model. The proportions of temporal probabilities > 0.5 yielded by the non-stationary model (10, 19, 28, and 38%) were greater than those of the stationary model (6, 10, 16, and 24%) for periods of 5, 10, 20, and 50 years, respectively. The 1D-CNN model (AUC = 84.1%) outperformed logistic regression (AUC = 80.1%) and naïve Bayes (AUC = 80.1%) models. The landslide hazard probability obtained from the non-stationary model is more susceptible than that of the stationary model. These results indicate that the proposed approach is a valuable tool for future landslide risk assessment and may be applicable even in areas without a landslide inventory.</p>","PeriodicalId":17938,"journal":{"name":"Landslides","volume":"72 1","pages":""},"PeriodicalIF":5.8000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A hybrid approach combining physics-based model with extreme value analysis for temporal probability of rainfall-triggered landslide\",\"authors\":\"Ho-Hong-Duy Nguyen, Ananta Man Singh Pradhan, Chang-Ho Song, Ji-Sung Lee, Yun-Tae Kim\",\"doi\":\"10.1007/s10346-024-02366-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The interplay between climate change–induced extreme rainfall and slope failure mechanisms presents a significant challenge. To address this, a new temporal modeling of landslides that integrates dynamic rainfall patterns with slope failure mechanisms is proposed. The approach features three steps: (1) analysis of the critical continuous rainfall (CCR) level using a physics-based model with Monte Carlo simulation; (2) calculation of the cumulative distribution function of the generalized extreme value distribution; and (3) estimation of the temporal probability map. Then, combined with the landslide spatial probability obtained from one-dimensional convolution neural network (1D-CNN), the landslide hazard probability was estimated for future periods of 5, 10, 20, and 50 years. The CCR and spatial probability maps were validated using the 2018 landslide event in Hiroshima Prefecture, Japan. The CCR map achieves an area under the receiver operating curve (AUC) of 74.8%. Cohesion and friction angle are the most sensitive in the hybrid model. The proportions of temporal probabilities > 0.5 yielded by the non-stationary model (10, 19, 28, and 38%) were greater than those of the stationary model (6, 10, 16, and 24%) for periods of 5, 10, 20, and 50 years, respectively. The 1D-CNN model (AUC = 84.1%) outperformed logistic regression (AUC = 80.1%) and naïve Bayes (AUC = 80.1%) models. The landslide hazard probability obtained from the non-stationary model is more susceptible than that of the stationary model. These results indicate that the proposed approach is a valuable tool for future landslide risk assessment and may be applicable even in areas without a landslide inventory.</p>\",\"PeriodicalId\":17938,\"journal\":{\"name\":\"Landslides\",\"volume\":\"72 1\",\"pages\":\"\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2024-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Landslides\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1007/s10346-024-02366-x\",\"RegionNum\":2,\"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":"Landslides","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s10346-024-02366-x","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, GEOLOGICAL","Score":null,"Total":0}
A hybrid approach combining physics-based model with extreme value analysis for temporal probability of rainfall-triggered landslide
The interplay between climate change–induced extreme rainfall and slope failure mechanisms presents a significant challenge. To address this, a new temporal modeling of landslides that integrates dynamic rainfall patterns with slope failure mechanisms is proposed. The approach features three steps: (1) analysis of the critical continuous rainfall (CCR) level using a physics-based model with Monte Carlo simulation; (2) calculation of the cumulative distribution function of the generalized extreme value distribution; and (3) estimation of the temporal probability map. Then, combined with the landslide spatial probability obtained from one-dimensional convolution neural network (1D-CNN), the landslide hazard probability was estimated for future periods of 5, 10, 20, and 50 years. The CCR and spatial probability maps were validated using the 2018 landslide event in Hiroshima Prefecture, Japan. The CCR map achieves an area under the receiver operating curve (AUC) of 74.8%. Cohesion and friction angle are the most sensitive in the hybrid model. The proportions of temporal probabilities > 0.5 yielded by the non-stationary model (10, 19, 28, and 38%) were greater than those of the stationary model (6, 10, 16, and 24%) for periods of 5, 10, 20, and 50 years, respectively. The 1D-CNN model (AUC = 84.1%) outperformed logistic regression (AUC = 80.1%) and naïve Bayes (AUC = 80.1%) models. The landslide hazard probability obtained from the non-stationary model is more susceptible than that of the stationary model. These results indicate that the proposed approach is a valuable tool for future landslide risk assessment and may be applicable even in areas without a landslide inventory.
期刊介绍:
Landslides are gravitational mass movements of rock, debris or earth. They may occur in conjunction with other major natural disasters such as floods, earthquakes and volcanic eruptions. Expanding urbanization and changing land-use practices have increased the incidence of landslide disasters. Landslides as catastrophic events include human injury, loss of life and economic devastation and are studied as part of the fields of earth, water and engineering sciences. The aim of the journal Landslides is to be the common platform for the publication of integrated research on landslide processes, hazards, risk analysis, mitigation, and the protection of our cultural heritage and the environment. The journal publishes research papers, news of recent landslide events and information on the activities of the International Consortium on Landslides.
- Landslide dynamics, mechanisms and processes
- Landslide risk evaluation: hazard assessment, hazard mapping, and vulnerability assessment
- Geological, Geotechnical, Hydrological and Geophysical modeling
- Effects of meteorological, hydrological and global climatic change factors
- Monitoring including remote sensing and other non-invasive systems
- New technology, expert and intelligent systems
- Application of GIS techniques
- Rock slides, rock falls, debris flows, earth flows, and lateral spreads
- Large-scale landslides, lahars and pyroclastic flows in volcanic zones
- Marine and reservoir related landslides
- Landslide related tsunamis and seiches
- Landslide disasters in urban areas and along critical infrastructure
- Landslides and natural resources
- Land development and land-use practices
- Landslide remedial measures / prevention works
- Temporal and spatial prediction of landslides
- Early warning and evacuation
- Global landslide database