GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Issue 2 (15th December 2017)

• Record Type: Journal Article
• Authors: Chen, Wei; Xie, Xiaoshen; Peng, Jianbing; Wang, Jiale; Duan, Zhao; Hong, Haoyuan
• Is Part Of: Geomatics, natural hazards & risk. Volume 8:Issue 2(2017)
• Page Start: 950
• Page End: 973
• Publication Date: 2017-12-15
• Subjects: Landslide susceptibility -- kernel logistic regression -- Naïve-Bayes tree -- alternating decision tree -- China; Geomatics -- Periodicals; Geomatics; Periodicals; 526.905
• DOI: 10.1080/19475705.2017.1289250
• Languages: English
• ISSNs: 1947-5705

Abstract

ABSTRACT: The main purpose of this paper is to explore some potential applications of sophisticated machine learning techniques such as the kernel logistic regression, Naïve-Bayes tree and alternating decision tree models for landslide susceptibility analysis at Taibai county (China). Initially, a landslide inventory map containing the information of 212 historical landslide locations was prepared. Seventy percentage (148) of landslides were randomly selected for training models and the remaining were used for validation. Additionally, 12 landslide conditioning factors were considered and the thematic layers were prepared in GIS. Subsequently, these three models were applied to build landslide susceptibility maps. The performances of the models were compared using the receive operating characteristic curves, kappa index, and statistical evaluation measures. The results show that the KLR model has the highest AUC values of 0.910 and 0.936 for training and validation datasets, respectively. The KLR model also has the highest degree of goodness-of-fits (84.5%) for the training dataset. The NBTree model has the highest goodness-of-fits (91.4%) for the validation dataset. However, the KLR model has the preferable balance performance for both the training and validation process. The results of this study demonstrate the benefit of selecting the optimal machine learning techniques in landslide susceptibility mapping.