Comparison of Machine Learning Methods for Potential Active Landslide Hazards Identification with Multi-Source Data
The early identification of potential landslide hazards is of great practical significance for disaster early warning and prevention. The study used different machine learning methods to identify potential active landslides along a 15 km buffer zone on both sides of Jinsha River (Panzhihua-Huize sec...
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| Format: | Article |
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MDPI AG
2021-04-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/4/253 |
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| author | Xiangxiang Zheng Guojin He Shanshan Wang Yi Wang Guizhou Wang Zhaoying Yang Junchuan Yu Ning Wang |
| author_facet | Xiangxiang Zheng Guojin He Shanshan Wang Yi Wang Guizhou Wang Zhaoying Yang Junchuan Yu Ning Wang |
| author_sort | Xiangxiang Zheng |
| collection | DOAJ |
| description | The early identification of potential landslide hazards is of great practical significance for disaster early warning and prevention. The study used different machine learning methods to identify potential active landslides along a 15 km buffer zone on both sides of Jinsha River (Panzhihua-Huize section), China. The morphology and texture features of landslides were characterized with InSAR deformation monitoring data and high-resolution optical remote sensing data, combined with 17 landslide influencing factors. In the study area, 83 deformation accumulation areas of potential landslide hazards and 54 deformation accumulation areas of non-potential landslide hazards were identified through spatial overlay analysis with 64 potential active landslides, which have been confirmed by field verification. The Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms were trained and tested through attribute selection and parameter optimization. Among the 17 landslide influencing factors, Drainage Density, NDVI, Slope and Weathering Degree play an indispensable role in the machine learning and recognition of landslide hazards in our study area, while other influencing factors play a certain role in different algorithms. A multi-index (Precision, Recall, F1) comparison shows that the SVM (0.867, 0.829, 0.816) has better recognition precision skill for small-scale unbalanced landslide deformation datasets, followed by RF (0.765, 0.756, 0.741), DT (0.755, 0.756, 0.748) and NB (0.659, 0.659, 0.659). Different from the previous study on landslide susceptibility and hazard mapping based on machine learning, this study focuses on how to find out the potential active landslide points more accurately, rather than evaluating the landslide susceptibility of specific areas to tell us which areas are more sensitive to landslides. This study verified the feasibility of early identification of landslide hazards by using different machine learning methods combined with deformation information and multi-source landslide influencing factors rather than by relying on human–computer interaction. This study shows that the efficiency of potential hazard identification can be increased while reducing the subjective bias caused by relying only on human experts. |
| first_indexed | 2024-03-10T12:29:29Z |
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| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T12:29:29Z |
| publishDate | 2021-04-01 |
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| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-f6c48890563f4c69971f817e508509f92023-11-21T14:50:04ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-04-0110425310.3390/ijgi10040253Comparison of Machine Learning Methods for Potential Active Landslide Hazards Identification with Multi-Source DataXiangxiang Zheng0Guojin He1Shanshan Wang2Yi Wang3Guizhou Wang4Zhaoying Yang5Junchuan Yu6Ning Wang7Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaChina Aero Geophysical Survey & Remote Sensing Center for Natural Resources, Beijing 100083, ChinaChina Aero Geophysical Survey & Remote Sensing Center for Natural Resources, Beijing 100083, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaChina Aero Geophysical Survey & Remote Sensing Center for Natural Resources, Beijing 100083, ChinaChina Aero Geophysical Survey & Remote Sensing Center for Natural Resources, Beijing 100083, ChinaChina Aero Geophysical Survey & Remote Sensing Center for Natural Resources, Beijing 100083, ChinaThe early identification of potential landslide hazards is of great practical significance for disaster early warning and prevention. The study used different machine learning methods to identify potential active landslides along a 15 km buffer zone on both sides of Jinsha River (Panzhihua-Huize section), China. The morphology and texture features of landslides were characterized with InSAR deformation monitoring data and high-resolution optical remote sensing data, combined with 17 landslide influencing factors. In the study area, 83 deformation accumulation areas of potential landslide hazards and 54 deformation accumulation areas of non-potential landslide hazards were identified through spatial overlay analysis with 64 potential active landslides, which have been confirmed by field verification. The Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) algorithms were trained and tested through attribute selection and parameter optimization. Among the 17 landslide influencing factors, Drainage Density, NDVI, Slope and Weathering Degree play an indispensable role in the machine learning and recognition of landslide hazards in our study area, while other influencing factors play a certain role in different algorithms. A multi-index (Precision, Recall, F1) comparison shows that the SVM (0.867, 0.829, 0.816) has better recognition precision skill for small-scale unbalanced landslide deformation datasets, followed by RF (0.765, 0.756, 0.741), DT (0.755, 0.756, 0.748) and NB (0.659, 0.659, 0.659). Different from the previous study on landslide susceptibility and hazard mapping based on machine learning, this study focuses on how to find out the potential active landslide points more accurately, rather than evaluating the landslide susceptibility of specific areas to tell us which areas are more sensitive to landslides. This study verified the feasibility of early identification of landslide hazards by using different machine learning methods combined with deformation information and multi-source landslide influencing factors rather than by relying on human–computer interaction. This study shows that the efficiency of potential hazard identification can be increased while reducing the subjective bias caused by relying only on human experts.https://www.mdpi.com/2220-9964/10/4/253multi-source datalandslidepotential geological hazardsmachine learning |
| spellingShingle | Xiangxiang Zheng Guojin He Shanshan Wang Yi Wang Guizhou Wang Zhaoying Yang Junchuan Yu Ning Wang Comparison of Machine Learning Methods for Potential Active Landslide Hazards Identification with Multi-Source Data ISPRS International Journal of Geo-Information multi-source data landslide potential geological hazards machine learning |
| title | Comparison of Machine Learning Methods for Potential Active Landslide Hazards Identification with Multi-Source Data |
| title_full | Comparison of Machine Learning Methods for Potential Active Landslide Hazards Identification with Multi-Source Data |
| title_fullStr | Comparison of Machine Learning Methods for Potential Active Landslide Hazards Identification with Multi-Source Data |
| title_full_unstemmed | Comparison of Machine Learning Methods for Potential Active Landslide Hazards Identification with Multi-Source Data |
| title_short | Comparison of Machine Learning Methods for Potential Active Landslide Hazards Identification with Multi-Source Data |
| title_sort | comparison of machine learning methods for potential active landslide hazards identification with multi source data |
| topic | multi-source data landslide potential geological hazards machine learning |
| url | https://www.mdpi.com/2220-9964/10/4/253 |
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