Tên bài báo:
Evaluation and Comparison of LogitBoost Ensemble, Fisher's Linear Discriminant Analysis, Logistic Regression, and Support Vector Machines Methods for Landslide Susceptibility Mapping- Tác giả:
- Phạm Thái Bình
- Tham gia cùng:
- Tạp chí:
- Geocarto International (United Kingdom)
- Năm xuất bản:
- 2017
- Trang:
- Từ trang 1 đến trang 19
- Lĩnh vực:
- Kỹ thuật xây dựng công trình giao thông
- Phạm vi:
- Quốc tế
Tóm tắt:
The purpose of this study was to investigate and compare the capabilities of four machine learning methods namely LogitBoost Ensemble (LBE), Fisher’s Linear Discriminate Analysis (FLDA), Logistic Regression (LR) and Support Vector Machines (SVM) to select the best method for landslide susceptibility mapping. A part of landslide prone area of Tehri Garhwal district of Uttarakhand state, India, was selected as a case study. Validation of models was carried out using statistical analysis, the chi square test and the Receiver Operating Characteristic (ROC) curve. Result analysis shows that the LBE has the highest prediction ability (AUC = 0.972) for landslide susceptibility mapping, followed by the SVM (0.945), the LR (0.873) and the FLDA (0.870), respectively. Therefore, the LBE is the best and a promising method in comparison to other three models for landslide susceptibility mapping.
