Tên bài báo:

A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers
Tác giả:
Phạm Thái Bình
Tham gia cùng:
Trần Trung Hiếu
Tạp chí:
Geocarto International
Năm xuất bản:
Từ trang 1 đến trang 25
Lĩnh vực:
Kỹ thuật xây dựng công trình giao thông
Phạm vi:
Quốc tế

Tóm tắt:

In the present study, Rotation Forest ensemble was integrated with different base classifiers to develop different hybrid models namely Rotation Forest based Support Vector Machines (RFSVM), Rotation Forest based Artificial Neural Networks (RFANN), Rotation Forest based Decision Trees (RFDT), and Rotation Forest based Naïve Bayes (RFNB) for landslide susceptibility modelling. The validity of these models was evaluated using statistical methods such as Root Mean Square Error (RMSE), Kappa index, accuracy, and the area under the success rate and predictive rate curves (AUC). Part of the landslide prone area of Pithoragarh district, Uttarakhand, Himalaya, India was selected as the study area. Results indicate that the RFDT is the best model showing the highest predictive capability (AUC ¼0.741) in comparison to RFANN (AUC¼0.710), RFSVM (AUC¼0.701), and RFNB (AUC¼ 0.640) models. The present study would be helpful in the selection of best model for landslide susceptibility mapping.

Từ khóa:

landslide susceptibility mapping machine learning rotation forest base classifiers India
Thông tin tác giả
Phạm Thái Bình

Phạm Thái Bình

Thạc sĩ kỹ thuật

Lý lịch khoa học