Combination of Conditioning Factors for Generation of Landslide Susceptibility Maps by Extreme Gradient Boosting in Cuenca, Ecuador
Landslides are hazardous events that occur mainly in mountainous areas and cause substantial losses of various kinds worldwide; therefore, it is important to investigate them. In this study, a specific Machine Learning (ML) method was further analyzed due to the good results obtained in the previous...
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2025-04-01
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| author | Esteban Bravo-López Tomás Fernández Chester Sellers Jorge Delgado-García |
| author_facet | Esteban Bravo-López Tomás Fernández Chester Sellers Jorge Delgado-García |
| author_sort | Esteban Bravo-López |
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| description | Landslides are hazardous events that occur mainly in mountainous areas and cause substantial losses of various kinds worldwide; therefore, it is important to investigate them. In this study, a specific Machine Learning (ML) method was further analyzed due to the good results obtained in the previous stage of this research. The algorithm implemented is Extreme Gradient Boosting (XGBoost), which was used to evaluate the susceptibility to landslides recorded in the city of Cuenca (Ecuador) and its surroundings, generating the respective Landslide Susceptibility Maps (LSM). For the model implementation, a landslide inventory updated to 2019 was used and several sets from 15 available conditioning factors were considered, applying two different methods of random point sampling. Additionally, a hyperparameter tuning process of XGBoost has been employed in order to optimize the predictive and computational performance of each model. The results obtained were validated using AUC-ROC, F-Score and the degree of landslide coincidence adjustment at high and very high susceptibility levels, showing a good predictive capacity in most cases. The best results were obtained with the set of the six best conditioning factors previously determined, as it produced good values in validation metrics (AUC = 0.83; F-Score = 0.73) and a degree of coincidence of landslides in the high and very high susceptibility levels above 90%. The Wilcoxon text led to establishing significant differences between methods. These results show the need to perform susceptibility analyses with different data sets to determine the most appropriate ones. |
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| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-0511406a4ff8466cb1ffa1654d984fe22025-08-20T02:33:43ZengMDPI AGAlgorithms1999-48932025-04-0118525810.3390/a18050258Combination of Conditioning Factors for Generation of Landslide Susceptibility Maps by Extreme Gradient Boosting in Cuenca, EcuadorEsteban Bravo-López0Tomás Fernández1Chester Sellers2Jorge Delgado-García3Department of Cartographic, Geodetic and Photogrammetric Engineering, Centre for Advanced Studies in Earth Sciences, Energy and Environment, University of Jaen, 23071 Jaen, SpainDepartment of Cartographic, Geodetic and Photogrammetric Engineering, Centre for Advanced Studies in Earth Sciences, Energy and Environment, University of Jaen, 23071 Jaen, SpainInstituto de Estudios de Régimen Seccional del Ecuador (IERSE), Vicerrectorado de Investigaciones, Universidad del Azuay, Cuenca 010204, EcuadorDepartment of Cartographic, Geodetic and Photogrammetric Engineering, Centre for Advanced Studies in Earth Sciences, Energy and Environment, University of Jaen, 23071 Jaen, SpainLandslides are hazardous events that occur mainly in mountainous areas and cause substantial losses of various kinds worldwide; therefore, it is important to investigate them. In this study, a specific Machine Learning (ML) method was further analyzed due to the good results obtained in the previous stage of this research. The algorithm implemented is Extreme Gradient Boosting (XGBoost), which was used to evaluate the susceptibility to landslides recorded in the city of Cuenca (Ecuador) and its surroundings, generating the respective Landslide Susceptibility Maps (LSM). For the model implementation, a landslide inventory updated to 2019 was used and several sets from 15 available conditioning factors were considered, applying two different methods of random point sampling. Additionally, a hyperparameter tuning process of XGBoost has been employed in order to optimize the predictive and computational performance of each model. The results obtained were validated using AUC-ROC, F-Score and the degree of landslide coincidence adjustment at high and very high susceptibility levels, showing a good predictive capacity in most cases. The best results were obtained with the set of the six best conditioning factors previously determined, as it produced good values in validation metrics (AUC = 0.83; F-Score = 0.73) and a degree of coincidence of landslides in the high and very high susceptibility levels above 90%. The Wilcoxon text led to establishing significant differences between methods. These results show the need to perform susceptibility analyses with different data sets to determine the most appropriate ones.https://www.mdpi.com/1999-4893/18/5/258landslidesmachine learninghyperparameter tuningconditioning factorsextreme gradient boostingCuenca (Ecuador) |
| spellingShingle | Esteban Bravo-López Tomás Fernández Chester Sellers Jorge Delgado-García Combination of Conditioning Factors for Generation of Landslide Susceptibility Maps by Extreme Gradient Boosting in Cuenca, Ecuador Algorithms landslides machine learning hyperparameter tuning conditioning factors extreme gradient boosting Cuenca (Ecuador) |
| title | Combination of Conditioning Factors for Generation of Landslide Susceptibility Maps by Extreme Gradient Boosting in Cuenca, Ecuador |
| title_full | Combination of Conditioning Factors for Generation of Landslide Susceptibility Maps by Extreme Gradient Boosting in Cuenca, Ecuador |
| title_fullStr | Combination of Conditioning Factors for Generation of Landslide Susceptibility Maps by Extreme Gradient Boosting in Cuenca, Ecuador |
| title_full_unstemmed | Combination of Conditioning Factors for Generation of Landslide Susceptibility Maps by Extreme Gradient Boosting in Cuenca, Ecuador |
| title_short | Combination of Conditioning Factors for Generation of Landslide Susceptibility Maps by Extreme Gradient Boosting in Cuenca, Ecuador |
| title_sort | combination of conditioning factors for generation of landslide susceptibility maps by extreme gradient boosting in cuenca ecuador |
| topic | landslides machine learning hyperparameter tuning conditioning factors extreme gradient boosting Cuenca (Ecuador) |
| url | https://www.mdpi.com/1999-4893/18/5/258 |
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