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

Abstract

(2017) 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, Geomatics, Natural Hazards and Risk, 8:2, 649-671, DOI: 10.1080/19475705.2016 ABSTRACTThe 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 Na€ ıve 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 D 0.846) and relatively high accuracy (ACC D 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.

Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance

“…Various methods such as IG [17], chi-square statistics [30], and Relief-F [29] have been proposed for feature selection in landslide modeling. In this study, the IG, proposed by Quinlan (1993) [40], was used to determine irrelevant and unimportant variables.…”

“…Among the machine learning methods, artificial neural network [19,20], fuzzy logic [21,22], neuro-fuzzy [23], support vector machine [24,25], random forest [26,27], and naïve Bayes tree [17,28] methods have been popularly applied.More recently, ensemble machine learning techniques have been used to enhance the prediction power and robustness of landslide susceptibility assessment. The ensemble methods, formed by a combination of variously based classifiers, have typically demonstrated significant improvement [17,24,29,30]. Ensemble techniques, which are relatively new approaches for producing a landslide susceptibility map, have been rarely used in the field.…”

“…More recently, ensemble machine learning techniques have been used to enhance the prediction power and robustness of landslide susceptibility assessment. The ensemble methods, formed by a combination of variously based classifiers, have typically demonstrated significant improvement [17,24,29,30]. Ensemble techniques, which are relatively new approaches for producing a landslide susceptibility map, have been rarely used in the field.…”

A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS

“…These systems can be established through combinations of two or more methods and techniques [36][37][38][39][40][41][42] or ensemble frameworks such as Stacking, Bagging, AdaBoost, Random Subspace, MultiBoost, Random Forests, Diverse DECORATE (Ensemble Creation by Oppositional Relabeling of Artificial Training Examples), and Rotation Forest [43,44]. Although these ensemble-based systems often improve performances of base classifiers, the Rotation Forest outperforms the others in term of accuracy and diversity in various datasets [43,45]. In addition, exploration of the Rotation Forest for landslide analysis has seldom been carried out.…”

An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method

“…Discrepancies among the results from different modeling approaches may be due to differences in model assumptions and algorithms. These common situations led to suggest using an ensemble of models for landslide susceptibility modeling, rather than relying on outputs from a single model to guide eradication efforts [31]. A random forest (RF) classification was adopted to combine the models and produce an ensemble landslide susceptibility map.…”