Abstract
ABSTRACTThe 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.
Highlights
Landslide is defined as the downslope movement of soil and rock affected by gravity (Malamud et al 2004)
The results show that the Na€ıve-Bayes tree (NBTree) has the highest classification accuracy (0.914) for the validation data-set, followed by the kernel logistic regression (KLR) model (0.898) and the alternating decision tree (ADTree) (0.820)
Spatial prediction of landslides is considered to be useful for land-use planning and the first important step in landslide hazard and risk assessment (Fell et al 2008)
Summary
Landslide is defined as the downslope movement of soil and rock affected by gravity (Malamud et al 2004) It is one of the most frequent and catastrophic geologic hazards causing enormous casualties and severe economic losses (Mahalingam & Olsen 2016; Mahalingam et al 2016; Myronidis et al 2016; Pradhan et al 2016). The physically based methods evaluate the safety factor of slopes based on the detailed geomorphic and geologic properties at site-specific locations and generally provide accurate results. It would be quite expensive and not practical in terms of regional landslide susceptibility assessments
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