{"title":"利用机器学习算法对日本堆土坝翻坝进行定量风险评估","authors":"","doi":"10.1016/j.ijdrr.2024.104892","DOIUrl":null,"url":null,"abstract":"<div><div>Earth-fill dams serve as crucial agricultural structures in Japan and act as buffers against flooding. However, their failure often tends to cause even greater downstream damage. Consequently, there is an urgent need for a quantitative assessment of the risks to earth-fill dams posed by disasters. The current detailed method of assessment is complicated, labour-intensive, and costly; hence, constructing risk surrogate models will greatly reduce the workload. This study employs two machine learning methods, GPR (Gaussian Process Regression) and XGBoost (eXtreme Gradient Boost), to develop surrogate models for assessing the damage cost and overtopping probability for 70 earth-fill dams in Okayama and Hiroshima prefectures, Japan. The predictive performance of each model was quantified by comparing the results against those of the detailed method. From the results, XGBoost demonstrates superior performance compared to GPR based on the comparison of coefficient of determination (R<sup>2</sup>) and root mean square error (RMSE). To clarify the extent to which the variables influence the XGBoost model, the SHapley Additive exPlanations (SHAP) algorithm was implemented. It offers an efficient and interpretable avenue for earth-fill dam risk assessments.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantitative risk assessment for overtopping of earth-fill dams in Japan using machine learning algorithms\",\"authors\":\"\",\"doi\":\"10.1016/j.ijdrr.2024.104892\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Earth-fill dams serve as crucial agricultural structures in Japan and act as buffers against flooding. However, their failure often tends to cause even greater downstream damage. Consequently, there is an urgent need for a quantitative assessment of the risks to earth-fill dams posed by disasters. The current detailed method of assessment is complicated, labour-intensive, and costly; hence, constructing risk surrogate models will greatly reduce the workload. This study employs two machine learning methods, GPR (Gaussian Process Regression) and XGBoost (eXtreme Gradient Boost), to develop surrogate models for assessing the damage cost and overtopping probability for 70 earth-fill dams in Okayama and Hiroshima prefectures, Japan. The predictive performance of each model was quantified by comparing the results against those of the detailed method. From the results, XGBoost demonstrates superior performance compared to GPR based on the comparison of coefficient of determination (R<sup>2</sup>) and root mean square error (RMSE). To clarify the extent to which the variables influence the XGBoost model, the SHapley Additive exPlanations (SHAP) algorithm was implemented. It offers an efficient and interpretable avenue for earth-fill dam risk assessments.</div></div>\",\"PeriodicalId\":13915,\"journal\":{\"name\":\"International journal of disaster risk reduction\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of disaster risk reduction\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S221242092400654X\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of disaster risk reduction","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221242092400654X","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
Quantitative risk assessment for overtopping of earth-fill dams in Japan using machine learning algorithms
Earth-fill dams serve as crucial agricultural structures in Japan and act as buffers against flooding. However, their failure often tends to cause even greater downstream damage. Consequently, there is an urgent need for a quantitative assessment of the risks to earth-fill dams posed by disasters. The current detailed method of assessment is complicated, labour-intensive, and costly; hence, constructing risk surrogate models will greatly reduce the workload. This study employs two machine learning methods, GPR (Gaussian Process Regression) and XGBoost (eXtreme Gradient Boost), to develop surrogate models for assessing the damage cost and overtopping probability for 70 earth-fill dams in Okayama and Hiroshima prefectures, Japan. The predictive performance of each model was quantified by comparing the results against those of the detailed method. From the results, XGBoost demonstrates superior performance compared to GPR based on the comparison of coefficient of determination (R2) and root mean square error (RMSE). To clarify the extent to which the variables influence the XGBoost model, the SHapley Additive exPlanations (SHAP) algorithm was implemented. It offers an efficient and interpretable avenue for earth-fill dam risk assessments.
期刊介绍:
The International Journal of Disaster Risk Reduction (IJDRR) is the journal for researchers, policymakers and practitioners across diverse disciplines: earth sciences and their implications; environmental sciences; engineering; urban studies; geography; and the social sciences. IJDRR publishes fundamental and applied research, critical reviews, policy papers and case studies with a particular focus on multi-disciplinary research that aims to reduce the impact of natural, technological, social and intentional disasters. IJDRR stimulates exchange of ideas and knowledge transfer on disaster research, mitigation, adaptation, prevention and risk reduction at all geographical scales: local, national and international.
Key topics:-
-multifaceted disaster and cascading disasters
-the development of disaster risk reduction strategies and techniques
-discussion and development of effective warning and educational systems for risk management at all levels
-disasters associated with climate change
-vulnerability analysis and vulnerability trends
-emerging risks
-resilience against disasters.
The journal particularly encourages papers that approach risk from a multi-disciplinary perspective.