Tên bài báo:

A novel ensemble classifier of rotation forest and Naïve bayer for susceptibility assessment at the Luc Yen district, Yen Bai Province (Vietnam) using GIS
Tác giả:
Phạm Thái Bình
Tham gia cùng:
Tạp chí:
Geomatics, Natural Hazards and Risk
Năm xuất bản:
Từ trang 1 đến trang 23
Lĩnh vực:
Kỹ thuật xây dựng công trình giao thông
Phạm vi:
Quốc tế

Tóm tắt:

The objective of this study is to attempt a new soft computing approach for assessment of landslide susceptibility in the Luc Yen district, Yen Bai province (Viet Nam) using a novel classifier ensemble model of Naive Bayes and Rotation Forest. First, history of 95 landslide locations was identified byfield investigations and interpretation of aerial photos. Also, the total ten landslide causal factors were selected (slope, aspect, elevation, curvature, lithology, land use, distance to roads, distance to rivers, distance to faults, and rainfall) to evaluate the spatial relationship with landslide occurrences. Information Gain technique is carried out to quantify the predictive capability of these factors. Second, landslide susceptibility assessment was carried out utilizing the novel classifier ensemble model. Finally, the performance of landslide model was validated using receiver operating characteristic curve technique, and statistical index-based evaluations. The novel classifier ensemble model indicates high prediction capability (AUC = 0.846) and relatively high accuracy (ACC = 78.77%). The study reveals that this model performs well in comparison to the other landslide models such as AdaBoost, Bagging, MultiBoost, and Random Forest. Overall, the novel classifier ensemble model is a promising method that could be used for landslide susceptibility assessment.

Từ khóa:

Landslides GIS Naive Bayes Rotation Forest; Viet Nam
Thông tin tác giả
Phạm Thái Bình

Phạm Thái Bình

Thạc sĩ kỹ thuật

Lý lịch khoa học