Improving Voting Feature Intervals for Spatial Prediction of Landslides

被引:10
|
作者
Binh Thai Pham [1 ]
Tran Van Phong [2 ]
Avand, Mohammadtaghi [3 ]
Al-Ansari, Nadhir [4 ]
Singh, Sushant K. [5 ]
Hiep Van Le [6 ]
Prakash, Indra [7 ]
机构
[1] Univ Transport Technol, Hanoi 100000, Vietnam
[2] Vietnam Acad Sci & Technol, Inst Geol Sci, 84 Chua Lang St, Hanoi 100000, Vietnam
[3] TarbiatModares Univ, Dept Watershed Management Engn, Coll Nat Resources, Tehran 14115111, Iran
[4] Lulea Univ Technol, Dept Civil Environm & Nat Resources Engn, S-97187 Lulea, Sweden
[5] Virtusa Corp, Hlth Care & Life Sci, Artificial Intelligence & Analyt, New York, NY USA
[6] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
[7] DDG R Geol Survey India, Gandhinagar 382010, India
关键词
LOGISTIC-REGRESSION; SUSCEPTIBILITY ASSESSMENT; DECISION TREE; MACHINE; MODELS; GIS; RAINFALL; ENTROPY; WEIGHT; INDEX;
D O I
10.1155/2020/4310791
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this study, the main aim is to improve performance of the voting feature intervals (VFIs), which is one of the most effective machine learning models, using two robust ensemble techniques, namely, AdaBoost and MultiBoost for landslide susceptibility assessment and prediction. For this, two hybrid models, namely, AdaBoost-based Voting Feature Intervals (ABVFIs) and MultiBoost-based Voting Feature Intervals (MBVFIs) were developed and validated using landslide data collected from one of the landslide affected districts of Vietnam, namely, Muong Lay. Quantitative validation methods including area under the ROC curve (AUC) were used to evaluate model performance. The results indicated that both the newly developed ensemble models ABVFI (AUC = 0.859) and MBVFI (AUC = 0.839) outperformed the single VFI (AUC = 0.824) model. Thus, ensemble framework-based VFI algorithms can be used for the accurate spatial prediction of landslides, which can also be applied in other landslide prone regions of the world. Landslide susceptibility maps developed by ensemble VFI models can be used for better landslide prevention and risk management of the area.
引用
收藏
页数:15
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