GIS-based landslide susceptibility mapping in the Longmen Mountain area (China) using three different machine learning algorithms and their comparison
文献类型：期刊论文
| 作者 | Huang, Ziyan1,4; Peng, Li1,3,4; Li, Sainan1,4; Liu, Ying1,4; Zhou, Shuang2 |
| 刊名 | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
 |
| 出版日期 | 2023-07-13 |
| 页码 | 15 |
| ISSN号 | 0944-1344 |
| 关键词 | Landslide susceptibility assessment Random forest model Frequency ratio method Support vector machine model Longmen Mountain area |
| DOI | 10.1007/s11356-023-28730-3 |
| 通讯作者 | Peng, Li(Pengli@imde.ac.cn) |
| 英文摘要 | Landslides are a common natural disaster, having severe socio-economic effects and posing immense threat to safety, such as loss of life at a global scale. Modeling and predicting the possibility of landslides are important in order to monitor and prevent their negative consequences. In this study, landslides are the primary research object. Further, the frequency ratio (FR) method was applied to the random forest (RF), support vector machine (SVM), and decision tree (DT) regression algorithms for landslide sensitivity assessment. It was also applied to landslide risk assessment mapping in the Longmen Mountain area. Therefore, taking into account the positive and negative sample balance, 7774 historical landslide points and 7774 non-landslide points were selected and divided them into training sets and test sets. The influence factors were selected and analyzed through multicollinearity analysis and the FR method. To improve the performance of the model and the accuracy of the findings, the individual environmental factors are normalized. Subsequently, the LSI (landslide susceptibility index), was obtained by calculating the frequency ratio. Following this, the RF, SVM, and DT were used to construct the model. The trained model calculates the landslide probability of each cell in the study area and generates the resultant susceptibility map. The receiver operating characteristic (ROC) curve and R-2 of this region were calculated to evaluate the model's performance. The results indicate that RF obtained the highest predictive performance (area under the curve (AUC) = 0.82) in landslide risk prediction, followed by SVM (AUC = 0.8) and DT (AUC = 0.69). The results of this study serve as a predictive map for landslide susceptibility areas and provide critical support for the security of lives and property for the human and socio-economic development in the Longmen Mountain region. In addition, the experiment results reveal that the machine learning model based on the FR method can improve the accuracy and performance of methods in studies related to landslide susceptibility. The method is equally applicable to research in other fields. |
| WOS关键词 | FREQUENCY RATIO ; LOGISTIC-REGRESSION ; RANDOM FOREST ; MODELS ; EARTHQUAKE ; COMBINATION ; INVENTORY |
| 资助项目 | National Natural Science Foundation of China[42071222] ; Sichuan Science and Technology Program[2022JDJQ0015] ; Tianfu Qingcheng Program[ZX20220027] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| 出版者 | SPRINGER HEIDELBERG |
| WOS记录号 | WOS:001029760900023 |
| 资助机构 | National Natural Science Foundation of China ; Sichuan Science and Technology Program ; Tianfu Qingcheng Program |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/195894]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Peng, Li |
| 作者单位 | 1.Sichuan Normal Univ, Key Lab Land Resources Evaluat & Monitoring Southw, Minist Educ, Chengdu 610101, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China 4.Sichuan Normal Univ, Coll Geog & Resources, Chengdu 610101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Ziyan,Peng, Li,Li, Sainan,et al. GIS-based landslide susceptibility mapping in the Longmen Mountain area (China) using three different machine learning algorithms and their comparison[J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,2023:15. |
| APA | Huang, Ziyan,Peng, Li,Li, Sainan,Liu, Ying,&Zhou, Shuang.(2023).GIS-based landslide susceptibility mapping in the Longmen Mountain area (China) using three different machine learning algorithms and their comparison.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,15. |
| MLA | Huang, Ziyan,et al."GIS-based landslide susceptibility mapping in the Longmen Mountain area (China) using three different machine learning algorithms and their comparison".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2023):15. |
入库方式： OAI收割
来源：地理科学与资源研究所
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