Landslide susceptibility assessment based on an interpretable coupled FR-RF model: A case study of Longyan City, Fujian Province, Southeast China
ABSTRACT: To enhance the prediction accuracy of landslides in in Longyan City, China, this study developed a methodology for geologic hazard susceptibility assessment based on a coupled model composed of a Geographic Information System (GIS) with integrated spatial data, a frequency ratio (FR) model...
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| Format: | Article |
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KeAi Communications Co., Ltd.
2025-04-01
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| Series: | China Geology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096519225000692 |
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| author | Zong-yue Lu Gen-yuan Liu Xi-dong Zhao Kang Sun Yan-si Chen Zhi-hong Song Kai Xue Ming-shan Yang |
| author_facet | Zong-yue Lu Gen-yuan Liu Xi-dong Zhao Kang Sun Yan-si Chen Zhi-hong Song Kai Xue Ming-shan Yang |
| author_sort | Zong-yue Lu |
| collection | DOAJ |
| description | ABSTRACT: To enhance the prediction accuracy of landslides in in Longyan City, China, this study developed a methodology for geologic hazard susceptibility assessment based on a coupled model composed of a Geographic Information System (GIS) with integrated spatial data, a frequency ratio (FR) model, and a random forest (RF) model (also referred to as the coupled FR-RF model). The coupled FR-RF model was constructed based on the analysis of nine influential factors, including distance from roads, normalized difference vegetation index (NDVI), and slope. The performance of the coupled FR-RF model was assessed using metrics such as Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves, yielding Area Under the Curve (AUC) values of 0.93 and 0.95, which indicate high predictive accuracy and reliability for geological hazard forecasting. Based on the model predictions, five susceptibility levels were determined in the study area, providing crucial spatial information for geologic hazard prevention and control. The contributions of various influential factors to landslide susceptibility were determined using SHapley Additive exPlanations (SHAP) analysis and the Gini index, enhancing the model interpretability and transparency. Additionally, this study discussed the limitations of the coupled FR-RF model and the prospects for its improvement using new technologies. This study provides an innovative method and theoretical support for geologic hazard prediction and management, holding promising prospects for application. |
| format | Article |
| id | doaj-art-6e63935635bb40deb19d73dddeee1a32 |
| institution | DOAJ |
| issn | 2589-9430 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | China Geology |
| spelling | doaj-art-6e63935635bb40deb19d73dddeee1a322025-08-20T03:15:06ZengKeAi Communications Co., Ltd.China Geology2589-94302025-04-018228129410.31035/cg2024123Landslide susceptibility assessment based on an interpretable coupled FR-RF model: A case study of Longyan City, Fujian Province, Southeast ChinaZong-yue Lu0Gen-yuan Liu1Xi-dong Zhao2Kang Sun3Yan-si Chen4Zhi-hong Song5Kai Xue6Ming-shan Yang7Center for Geophysical Survey, China Geological Survey, Langfang 065000, China; Technology Innovation Center for Earth Near Surface Detection, China Geological Survey, Langfang 065000, ChinaCenter for Geophysical Survey, China Geological Survey, Langfang 065000, China; Technology Innovation Center for Earth Near Surface Detection, China Geological Survey, Langfang 065000, China; Corresponding authorHarbin Center for Integrated Natural Resources Survey, China Geological Survey, Harbin 150086, China; Observation and Research Station of Earth Critical Zone in Black Soil, Harbin, Ministry of Natural Resources 150086, China; Corresponding authorCenter for Geophysical Survey, China Geological Survey, Langfang 065000, China; Technology Innovation Center for Earth Near Surface Detection, China Geological Survey, Langfang 065000, ChinaCenter for Geophysical Survey, China Geological Survey, Langfang 065000, China; Technology Innovation Center for Earth Near Surface Detection, China Geological Survey, Langfang 065000, ChinaCenter for Geophysical Survey, China Geological Survey, Langfang 065000, China; Technology Innovation Center for Earth Near Surface Detection, China Geological Survey, Langfang 065000, ChinaCenter for Geophysical Survey, China Geological Survey, Langfang 065000, China; Technology Innovation Center for Earth Near Surface Detection, China Geological Survey, Langfang 065000, ChinaCenter for Geophysical Survey, China Geological Survey, Langfang 065000, China; Technology Innovation Center for Earth Near Surface Detection, China Geological Survey, Langfang 065000, ChinaABSTRACT: To enhance the prediction accuracy of landslides in in Longyan City, China, this study developed a methodology for geologic hazard susceptibility assessment based on a coupled model composed of a Geographic Information System (GIS) with integrated spatial data, a frequency ratio (FR) model, and a random forest (RF) model (also referred to as the coupled FR-RF model). The coupled FR-RF model was constructed based on the analysis of nine influential factors, including distance from roads, normalized difference vegetation index (NDVI), and slope. The performance of the coupled FR-RF model was assessed using metrics such as Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves, yielding Area Under the Curve (AUC) values of 0.93 and 0.95, which indicate high predictive accuracy and reliability for geological hazard forecasting. Based on the model predictions, five susceptibility levels were determined in the study area, providing crucial spatial information for geologic hazard prevention and control. The contributions of various influential factors to landslide susceptibility were determined using SHapley Additive exPlanations (SHAP) analysis and the Gini index, enhancing the model interpretability and transparency. Additionally, this study discussed the limitations of the coupled FR-RF model and the prospects for its improvement using new technologies. This study provides an innovative method and theoretical support for geologic hazard prediction and management, holding promising prospects for application.http://www.sciencedirect.com/science/article/pii/S2096519225000692Machine learningLandslide susceptibility assessmentGeographic Information System (GIS)Coupled FR-RF modelRandom forestInterpretability |
| spellingShingle | Zong-yue Lu Gen-yuan Liu Xi-dong Zhao Kang Sun Yan-si Chen Zhi-hong Song Kai Xue Ming-shan Yang Landslide susceptibility assessment based on an interpretable coupled FR-RF model: A case study of Longyan City, Fujian Province, Southeast China China Geology Machine learning Landslide susceptibility assessment Geographic Information System (GIS) Coupled FR-RF model Random forest Interpretability |
| title | Landslide susceptibility assessment based on an interpretable coupled FR-RF model: A case study of Longyan City, Fujian Province, Southeast China |
| title_full | Landslide susceptibility assessment based on an interpretable coupled FR-RF model: A case study of Longyan City, Fujian Province, Southeast China |
| title_fullStr | Landslide susceptibility assessment based on an interpretable coupled FR-RF model: A case study of Longyan City, Fujian Province, Southeast China |
| title_full_unstemmed | Landslide susceptibility assessment based on an interpretable coupled FR-RF model: A case study of Longyan City, Fujian Province, Southeast China |
| title_short | Landslide susceptibility assessment based on an interpretable coupled FR-RF model: A case study of Longyan City, Fujian Province, Southeast China |
| title_sort | landslide susceptibility assessment based on an interpretable coupled fr rf model a case study of longyan city fujian province southeast china |
| topic | Machine learning Landslide susceptibility assessment Geographic Information System (GIS) Coupled FR-RF model Random forest Interpretability |
| url | http://www.sciencedirect.com/science/article/pii/S2096519225000692 |
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