Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods

Haoyuan Hong, Himan Shahabi, Ataollah Shirzadi, Wei Chen, Kamran Chapi, Baharin Bin Ahmad, Majid Shadman Roodposhti, Arastoo Yari Hesar, Yingying Tian and Dieu Tien Bui ()
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Haoyuan Hong: Nanjing Normal University
Himan Shahabi: University of Kurdistan
Ataollah Shirzadi: University of Kurdistan
Wei Chen: Xi’an University of Science and Technology
Kamran Chapi: University of Kurdistan
Baharin Bin Ahmad: Universiti Teknologi Malaysia (UTM)
Majid Shadman Roodposhti: University of Tasmania
Arastoo Yari Hesar: University of Mohaghegh Ardabili
Yingying Tian: Jiangxi Provincial Meteorological Observatory, Jiangxi Meteorological Bureau
Dieu Tien Bui: Ton Duc Thang University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2019, vol. 96, issue 1, No 8, 173-212

Abstract: Abstract The aim of this research is to investigate multi-criteria decision making [spatial multi-criteria evaluation (SMCE)], bivariate statistical methods [frequency ratio (FR), index of entropy (IOE), weighted linear combination (WLC)] and machine learning [support vector machine (SVM)] models for estimating landslide susceptibility at the Wuning area, China. A total of 445 landslides were randomly classified into 70% (311 landslides) and 30% (134 landslides) to train and validate landslide models, respectively. Fourteen landslide conditioning factors including slope angle, slope aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, NDVI, land use, rainfall, distance to road, distance to river and distance to fault were then studied for landslide susceptibility assessment. Performances of five studied models were evaluated using area under the ROC curve (AUROC) for training (success rate curve) and validation (prediction rate curve) datasets, statistical-based measures and tests. Results indicated that the area under the success rate curve for the FR, IOE, WLC, SVM and SMCE models was 88.32%, 82.58%, 78.91%, 85.47% and 89.96%, respectively, demonstrating that SMCE could provide the higher accuracy. The prediction capability findings revealed that the SMCE model (AUC = 86.81%) was also the highest approach among the five studied models, followed by the FR (AUC = 84.53%), the SVM (AUC = 81.24%), the IOE (AUC = 79.67%) and WLC (73.92%) methods. The landslide susceptibility maps derived from the above five models are reasonably accurate and could be used to perform elementary land use planning for hazard extenuation.

Keywords: Landslide susceptibility; Natural disaster; Support vector machine; Spatial multi-criteria evaluation; Weighted linear combination (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s11069-018-3536-0

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