A python system for regional landslide susceptibility assessment by integrating machine learning models and its application
Landslide susceptibility assessment is considered the first step in landslide risk assessment, but current studies mostly rely on GIS platforms or other software for data preprocessing. The modeling process is relatively complicated and multi-models cannot be integrated. With regard to this issue, t...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023087509 |
| _version_ | 1797429859553640448 |
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| author | Zizheng Guo Fei Guo Yu Zhang Jun He Guangming Li Yufei Yang Xiaobo Zhang |
| author_facet | Zizheng Guo Fei Guo Yu Zhang Jun He Guangming Li Yufei Yang Xiaobo Zhang |
| author_sort | Zizheng Guo |
| collection | DOAJ |
| description | Landslide susceptibility assessment is considered the first step in landslide risk assessment, but current studies mostly rely on GIS platforms or other software for data preprocessing. The modeling process is relatively complicated and multi-models cannot be integrated. With regard to this issue, this study develops a Python system for automatic assessment of regional landslide susceptibility. The Python system implements landslide susceptibility assessment through three modules: geographic data processing, machine learning modeling and result evaluation analysis. For geographic data processing, ten landslide influencing factors can be used to construct an evaluation factor dataset and reclassify the thematic maps based on the frequency ratio method. Four built-in machine learning models (logistic regression (LR), multi-layer perceptron (MLP), support vector machine (SVM) and extreme gradient boosting (XGBoost)) are integrated into the system to complete susceptibility modeling and calculation. Additionally, receiver operating characteristic (ROC) curves can be automatically generated to evaluate the accuracy. The system was then applied into Lantian County in Shaanxi Province as a demonstration example. The results show that the areas under the ROC curve (AUC) of the four models are 0.838 (LR)、0.882 (SVM)、0.809 (MLP) and 0.812 (XGBoost), respectively, indicating that the SVM model was the most suitable model for landslide susceptibility assessment in Lantian County in the Loess Plateau of China. The system has now been made open source on Github, which can effectively improve the efficiency of regional landslide susceptibility assessment, especially provide tools for data processing and modeling for non-professionals. |
| first_indexed | 2024-03-09T09:18:58Z |
| format | Article |
| id | doaj.art-8f438b29919a4a0c9b975cc520f98aa4 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-09T09:18:58Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-8f438b29919a4a0c9b975cc520f98aa42023-12-02T07:02:43ZengElsevierHeliyon2405-84402023-11-01911e21542A python system for regional landslide susceptibility assessment by integrating machine learning models and its applicationZizheng Guo0Fei Guo1Yu Zhang2Jun He3Guangming Li4Yufei Yang5Xiaobo Zhang6Hubei Key Laboratory of Disaster Prevention and Mitigation (China Three Gorges University), Yichang, 443002, China; Key Laboratory of Geological Hazards on Three Gorges Reservoir Area (China Three Gorges University), Ministry of Education, Yichang, 443002, China; School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, 300401, ChinaHubei Key Laboratory of Disaster Prevention and Mitigation (China Three Gorges University), Yichang, 443002, China; Key Laboratory of Geological Hazards on Three Gorges Reservoir Area (China Three Gorges University), Ministry of Education, Yichang, 443002, China; Corresponding author. Hubei Key Laboratory of Disaster Prevention and Mitigation (China Three Gorges University), Yichang, 443002, China.Zhejiang Geology and Mineral Technology Co. LTD, Hangzhou, 310007, China; Wenzhou Engineering Survey Institute Co., LTD, Wenzhou, 325006, ChinaSchool of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, 300401, China; Corresponding author.Tianjin Municipal Engineering Design & Research Institute (TMEDI), Tianjin, 300392, ChinaSchool of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, 300401, ChinaBeijing Glory PKPM Technology Co.,Ltd., Beijing, 100013, ChinaLandslide susceptibility assessment is considered the first step in landslide risk assessment, but current studies mostly rely on GIS platforms or other software for data preprocessing. The modeling process is relatively complicated and multi-models cannot be integrated. With regard to this issue, this study develops a Python system for automatic assessment of regional landslide susceptibility. The Python system implements landslide susceptibility assessment through three modules: geographic data processing, machine learning modeling and result evaluation analysis. For geographic data processing, ten landslide influencing factors can be used to construct an evaluation factor dataset and reclassify the thematic maps based on the frequency ratio method. Four built-in machine learning models (logistic regression (LR), multi-layer perceptron (MLP), support vector machine (SVM) and extreme gradient boosting (XGBoost)) are integrated into the system to complete susceptibility modeling and calculation. Additionally, receiver operating characteristic (ROC) curves can be automatically generated to evaluate the accuracy. The system was then applied into Lantian County in Shaanxi Province as a demonstration example. The results show that the areas under the ROC curve (AUC) of the four models are 0.838 (LR)、0.882 (SVM)、0.809 (MLP) and 0.812 (XGBoost), respectively, indicating that the SVM model was the most suitable model for landslide susceptibility assessment in Lantian County in the Loess Plateau of China. The system has now been made open source on Github, which can effectively improve the efficiency of regional landslide susceptibility assessment, especially provide tools for data processing and modeling for non-professionals.http://www.sciencedirect.com/science/article/pii/S2405844023087509Landslide susceptibility assessmentPythonMachine learning modelsLoess plateauGIS |
| spellingShingle | Zizheng Guo Fei Guo Yu Zhang Jun He Guangming Li Yufei Yang Xiaobo Zhang A python system for regional landslide susceptibility assessment by integrating machine learning models and its application Heliyon Landslide susceptibility assessment Python Machine learning models Loess plateau GIS |
| title | A python system for regional landslide susceptibility assessment by integrating machine learning models and its application |
| title_full | A python system for regional landslide susceptibility assessment by integrating machine learning models and its application |
| title_fullStr | A python system for regional landslide susceptibility assessment by integrating machine learning models and its application |
| title_full_unstemmed | A python system for regional landslide susceptibility assessment by integrating machine learning models and its application |
| title_short | A python system for regional landslide susceptibility assessment by integrating machine learning models and its application |
| title_sort | python system for regional landslide susceptibility assessment by integrating machine learning models and its application |
| topic | Landslide susceptibility assessment Python Machine learning models Loess plateau GIS |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023087509 |
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