Landslide susceptibility based on statistical and machine learning methods: a case study of western Tibet plateau

Yongpeng Yang; Hao Chen; Ya Guo; Xin He; Yu Bian

doi:10.1117/12.3011682

7 December 2023 Landslide susceptibility based on statistical and machine learning methods: a case study of western Tibet plateau

Yongpeng Yang, Hao Chen, Ya Guo, Xin He, Yu Bian

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Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129415I (2023) https://doi.org/10.1117/12.3011682
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

The evaluation of landslide exposure plays a crucial role in estimating the risks associated with landslides and debris flows in a specific region, providing valuable insights for effective prevention and mitigation of geological hazards. The western Tibetan Plateau was chosen for this study from human interferences, and then this paper can obtain the comparison between the statistical and machine learning methods. Seven landslide factors were applied for the landslide susceptibility maps, including the slope, aspect, lithology, distance to faults, distance to rivers, distance to roads and elevation. In this study, the Information Value Model (IVM) and weight of evidence method were employed in conjunction with Logistic Regression (LR) and Multi-Layer Perceptron (MLP), utilizing IVM-LR, WOE-LR, IVM-MLP, and WOE-MLP approaches, to assess landslide hazards. The study area was divided into five hazard grades, namely very high, high, moderate, low, and very low, based on the generated susceptibility maps. The credibility level of all susceptibility maps produced by the models exceeded 85%, as revealed by a comparative analysis of Receiver Operating Characteristic (ROC) curves. Notably, the IVM-LR model exhibited superior performance in assessing landslide susceptibility in the study area.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Yongpeng Yang, Hao Chen, Ya Guo, Xin He, and Yu Bian "Landslide susceptibility based on statistical and machine learning methods: a case study of western Tibet plateau", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129415I (7 December 2023); https://doi.org/10.1117/12.3011682

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KEYWORDS

Machine learning

Data modeling

Lawrencium

Statistical analysis

Risk assessment

Roads

Performance modeling

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