A novel ensemble classifier of rotation forest and Naïve Bayer for landslide susceptibility assessment at the Luc Yen district, Yen Bai Province (Viet Nam) using GIS

• Main Authors: Binh Thai Pham, Dieu Tien Bui, M.B. Dholakia, Indra Prakash, Ha Viet Pham, Khalid Mehmood, Hung Quoc Le
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2017-12-01
• Series: Geomatics, Natural Hazards & Risk
• Subjects: landslides; gis; naïve bayes; rotation forest; viet nam
• Online Access: http://dx.doi.org/10.1080/19475705.2016.1255667
• ISSN: 1947-5705; 1947-5713

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 Naïve 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.