GIS-Based Comparative Study of the Bayesian Network, Decision Table, Radial Basis Function Network and Stochastic Gradient Descent for the Spatial Prediction of Landslide Susceptibility

Junpeng Huang, Sixiang Ling, Xiyong Wu and Rui Deng
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Junpeng Huang: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Sixiang Ling: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Xiyong Wu: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Rui Deng: China Railway Eryuan Engineering Group Co., Ltd., Chengdu 610031, China

Land, 2022, vol. 11, issue 3, 1-25

Abstract: Landslides frequently occur along the eastern margin of the Tibetan Plateau, which poses a risk to the construction, maintenance, and transportation of the proposed Dujiangyan city to Siguniang Mountain (DS) railway, China. Therefore, four advanced machine learning models, namely, the Bayesian network (BN), decision table (DTable), radial basis function network (RBFN), and stochastic gradient descent (SGD), are proposed in this study to delineate landslide susceptibility zones. First, a landslide inventory map was randomly divided into 828 (75%) samples and 276 (25%) samples for training and validation, respectively. Second, the One-R technique was utilized to analyze the importance of 14 variables. Then, the prediction capability of the four models was validated and compared in terms of different statistical indices (accuracy (ACC) and Cohen’s kappa coefficient ( k )) and the areas under the curve (AUC) in the receiver operating characteristic curve. The results showed that the SGD model performed best (AUC = 0.897, ACC = 80.98%, and k = 0.62), followed by the BN (AUC = 0.863, ACC = 78.80%, and k = 0.58), RBFN (AUC = 0.846, ACC = 77.36%, and k = 0.55), and DTable (AUC = 0.843, ACC = 76.45%, and k = 0.53) models. The susceptibility maps revealed that the DS railway segments from Puyang town to Dengsheng village are in high and very high-susceptibility zones.

Keywords: landslide susceptibility; machine learning; stochastic gradient descent; railway corridor; GIS; factor selection (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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