Tên bài báo:

A comparative study of different machine learning methods for landside susceptibility assessment: A case study of Uttarakhand area (India)
Tác giả:
Phạm Thái Bình
Tham gia cùng:
Tạp chí:
Environment Modelling & Software
Năm xuất bản:
Từ trang 240 đến trang 250
Lĩnh vực:
Kỹ thuật xây dựng công trình giao thông
Phạm vi:
Quốc tế

Tóm tắt:

Landslide susceptibility assessment of Uttarakhand area of India has been done by applying five machine learning methods namely Support Vector Machines (SVM), Logistic Regression (LR), Fisher's linear Discriminant Analysis (FLDA), Bayesian Network (BN), and Naive Bayes (NB). Performance of these methods has been evaluated using the ROC curve and statistical index based methods. Analysis and comparison of the results show that all five landslide models performed well for landslide susceptibility assessment (AUC = 0.910-0.950). However, it has been observed that the SVM model (AUC = 0.950) has the best performance in comparison to other landslide models, followed by the LR model (AUC = 0.922), the FLDA model (AUC = 0.921), the BN model (AUC = 0.915), and the NB model (AUC = 0.910), respectively.

Từ khóa:

India Uttarakhand Machine learning Landslides susceptibility assessment
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