{"title":"未来气候条件下基于机器学习的滑坡易发性评估的重要考虑因素","authors":"Yi Han, Shabnam J. Semnani","doi":"10.1007/s11440-024-02363-3","DOIUrl":null,"url":null,"abstract":"<p>Rainfall-induced landslides have caused a large amount of economic losses and casualties over the years. Machine learning techniques have been widely applied in recent years to assess landslide susceptibility over regions of interest. However, a number of challenges limit the reliability and performance of machine learning-based landslide models. In particular, class imbalance in the dataset, selection of landslide conditioning factors, and potential extrapolation problems for landslide prediction under future conditions need to be carefully addressed. In this work, we introduce methodologies to address these challenges using XGBoost to train the landslide prediction model. Data resampling techniques are adopted to improve the model performance with the imbalanced dataset. Various models are trained and their performances are evaluated using a combination of different metrics. The results show that synthetic minority oversampling technique combined with the proposed gridded hyperspace sampling technique performs better than the other imbalance learning techniques with XGBoost. Subsequently, the extrapolation performance of the XGBoost model is evaluated, showing that the predictions remain valid for the projected climate conditions. As a case study, landslide susceptibility maps in California, USA are generated using the developed model and are compared with the historical California landslide catalog. These results suggest that the developed model can be of great significance in global landslide susceptibility mapping under climate change scenarios.</p>","PeriodicalId":49308,"journal":{"name":"Acta Geotechnica","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Important considerations in machine learning-based landslide susceptibility assessment under future climate conditions\",\"authors\":\"Yi Han, Shabnam J. Semnani\",\"doi\":\"10.1007/s11440-024-02363-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Rainfall-induced landslides have caused a large amount of economic losses and casualties over the years. Machine learning techniques have been widely applied in recent years to assess landslide susceptibility over regions of interest. However, a number of challenges limit the reliability and performance of machine learning-based landslide models. In particular, class imbalance in the dataset, selection of landslide conditioning factors, and potential extrapolation problems for landslide prediction under future conditions need to be carefully addressed. In this work, we introduce methodologies to address these challenges using XGBoost to train the landslide prediction model. Data resampling techniques are adopted to improve the model performance with the imbalanced dataset. Various models are trained and their performances are evaluated using a combination of different metrics. The results show that synthetic minority oversampling technique combined with the proposed gridded hyperspace sampling technique performs better than the other imbalance learning techniques with XGBoost. Subsequently, the extrapolation performance of the XGBoost model is evaluated, showing that the predictions remain valid for the projected climate conditions. As a case study, landslide susceptibility maps in California, USA are generated using the developed model and are compared with the historical California landslide catalog. These results suggest that the developed model can be of great significance in global landslide susceptibility mapping under climate change scenarios.</p>\",\"PeriodicalId\":49308,\"journal\":{\"name\":\"Acta Geotechnica\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-08-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta Geotechnica\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s11440-024-02363-3\",\"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":"Acta Geotechnica","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11440-024-02363-3","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}
Important considerations in machine learning-based landslide susceptibility assessment under future climate conditions
Rainfall-induced landslides have caused a large amount of economic losses and casualties over the years. Machine learning techniques have been widely applied in recent years to assess landslide susceptibility over regions of interest. However, a number of challenges limit the reliability and performance of machine learning-based landslide models. In particular, class imbalance in the dataset, selection of landslide conditioning factors, and potential extrapolation problems for landslide prediction under future conditions need to be carefully addressed. In this work, we introduce methodologies to address these challenges using XGBoost to train the landslide prediction model. Data resampling techniques are adopted to improve the model performance with the imbalanced dataset. Various models are trained and their performances are evaluated using a combination of different metrics. The results show that synthetic minority oversampling technique combined with the proposed gridded hyperspace sampling technique performs better than the other imbalance learning techniques with XGBoost. Subsequently, the extrapolation performance of the XGBoost model is evaluated, showing that the predictions remain valid for the projected climate conditions. As a case study, landslide susceptibility maps in California, USA are generated using the developed model and are compared with the historical California landslide catalog. These results suggest that the developed model can be of great significance in global landslide susceptibility mapping under climate change scenarios.
期刊介绍:
Acta Geotechnica is an international journal devoted to the publication and dissemination of basic and applied research in geoengineering – an interdisciplinary field dealing with geomaterials such as soils and rocks. Coverage emphasizes the interplay between geomechanical models and their engineering applications. The journal presents original research papers on fundamental concepts in geomechanics and their novel applications in geoengineering based on experimental, analytical and/or numerical approaches. The main purpose of the journal is to foster understanding of the fundamental mechanisms behind the phenomena and processes in geomaterials, from kilometer-scale problems as they occur in geoscience, and down to the nano-scale, with their potential impact on geoengineering. The journal strives to report and archive progress in the field in a timely manner, presenting research papers, review articles, short notes and letters to the editors.