Ground deformation processes,such as landslides and subsidence,cause significant social,economic,and environmental impacts.This study aims to automatically classify ground deformation processes in Spain using a machin...Ground deformation processes,such as landslides and subsidence,cause significant social,economic,and environmental impacts.This study aims to automatically classify ground deformation processes in Spain using a machine learning approach applied to InSAR-based datasets.The database integrates InSAR measurement points(MPs)from 20 case studies in Spain,obtained from various institutional sources,and 32 geoenvironmental variables related to ground deformation,morphometry,geology,climate,and land use.The proposed classifi-cation strategy follows a hierarchical structure with two levels:first,distinguishing between landslides and subsidence;then,identifying the specific type within each main class(mining landslide,environmental landslide,constructive subsidence,mining subsidence,and piezometric subsidence).Several machine learning algorithms(Naïve Bayes,Logistic Regression,Decision Tree,Random Forest,Extra Trees,Gradient Boosting Machine,XGBoost,LightGBM,and CatBoost)and data configurations were tested,combining different spatial resolutions and class balancing techniques.The best performance(Cohen’s Kappa=0.78)was achieved with the hierar-chical approach using the 200 m grid dataset,applying XGBoost for the parental and landslide models,and CatBoost for the subsidence model.Using this approach,70%de test sites achieved over 88%correctly classified cells,20%had between 50%and 83%,and only one test case was entirely misclassified.The analysis of the most relevant variables indicates that annual mean precipitation,mining activity,buildings,landslide suscep-tibility,and slope are key factors.These results demonstrate the potential of the hierarchical approach to improve classification and lay the groundwork for future application at national and European scales,incor-porating new training cases,process types,and continental data sources.In conclusion,this study presents,for the first time,a hierarchical machine learning model capable of accurately classifying ground deformation processes in Spain,with the aim of supporting territorial management and geohazard mitigation.展开更多
基金supported by Grant PRE2021-100044,funded by MCIN/AEI/10.13039/501100011033“FSE invests in your future”by(SARAI)Project PID2020-116540RB-C22 funded by MCIN/AEI/10.13039/501100011033.
文摘Ground deformation processes,such as landslides and subsidence,cause significant social,economic,and environmental impacts.This study aims to automatically classify ground deformation processes in Spain using a machine learning approach applied to InSAR-based datasets.The database integrates InSAR measurement points(MPs)from 20 case studies in Spain,obtained from various institutional sources,and 32 geoenvironmental variables related to ground deformation,morphometry,geology,climate,and land use.The proposed classifi-cation strategy follows a hierarchical structure with two levels:first,distinguishing between landslides and subsidence;then,identifying the specific type within each main class(mining landslide,environmental landslide,constructive subsidence,mining subsidence,and piezometric subsidence).Several machine learning algorithms(Naïve Bayes,Logistic Regression,Decision Tree,Random Forest,Extra Trees,Gradient Boosting Machine,XGBoost,LightGBM,and CatBoost)and data configurations were tested,combining different spatial resolutions and class balancing techniques.The best performance(Cohen’s Kappa=0.78)was achieved with the hierar-chical approach using the 200 m grid dataset,applying XGBoost for the parental and landslide models,and CatBoost for the subsidence model.Using this approach,70%de test sites achieved over 88%correctly classified cells,20%had between 50%and 83%,and only one test case was entirely misclassified.The analysis of the most relevant variables indicates that annual mean precipitation,mining activity,buildings,landslide suscep-tibility,and slope are key factors.These results demonstrate the potential of the hierarchical approach to improve classification and lay the groundwork for future application at national and European scales,incor-porating new training cases,process types,and continental data sources.In conclusion,this study presents,for the first time,a hierarchical machine learning model capable of accurately classifying ground deformation processes in Spain,with the aim of supporting territorial management and geohazard mitigation.
