Landslide identification using machine learning

Haojie Wang; Limin Zhang; Kesheng Yin; Hongyu Luo; Jinhui Li

doi:10.1016/j.gsf.2020.02.012

Volume 12 Issue 1

Dec. 2020

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Haojie Wang, Limin Zhang, Kesheng Yin, Hongyu Luo, Jinhui Li. Landslide identification using machine learning[J]. Geoscience Frontiers, 2021, 12(1): 351-364. doi: 10.1016/j.gsf.2020.02.012

Citation:

Haojie Wang, Limin Zhang, Kesheng Yin, Hongyu Luo, Jinhui Li. Landslide identification using machine learning[J]. Geoscience Frontiers, 2021, 12(1): 351-364. doi: 10.1016/j.gsf.2020.02.012

Haojie Wang, Limin Zhang, Kesheng Yin, Hongyu Luo, Jinhui Li. Landslide identification using machine learning[J]. Geoscience Frontiers, 2021, 12(1): 351-364. doi: 10.1016/j.gsf.2020.02.012

Citation:

Haojie Wang, Limin Zhang, Kesheng Yin, Hongyu Luo, Jinhui Li. Landslide identification using machine learning[J]. Geoscience Frontiers, 2021, 12(1): 351-364. doi: 10.1016/j.gsf.2020.02.012

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Landslide identification using machine learning

doi: 10.1016/j.gsf.2020.02.012

a Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China;
b Department of Civil and Environmental Engineering, Harbin Institute of Technology(Shenzhen), Shenzhen 518055, China

Funds:

This research is supported by the Research Grants Council of the Hong Kong SAR Government (Nos. 16205719, AoE/E-603/18 and^-16206217).

Received Date: 2019-09-27
Rev Recd Date: 2019-12-10

Abstract

Abstract

Landslide identification is critical for risk assessment and mitigation. This paper proposes a novel machinelearning and deep-learning method to identify natural-terrain landslides using integrated geodatabases. First, landslide-related data are compiled, including topographic data, geological data and rainfall-related data. Then, three integrated geodatabases are established; namely, Recent Landslide Database (RecLD), Relict Landslide Database (RelLD) and Joint Landslide Database (JLD). After that, five machine learning and deep learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), boosting methods and convolutional neural network (CNN), are utilized and evaluated on each database. A case study in Lantau, Hong Kong, is conducted to demonstrate the application of the proposed method. From the results of the case study, CNN achieves an identification accuracy of 92.5% on RecLD, and outperforms other algorithms due to its strengths in feature extraction and multi dimensional data processing. Boosting methods come second in terms of accuracy, followed by RF, LR and SVM. By using machine learning and deep learning techniques, the proposed landslide identification method shows outstanding robustness and great potential in tackling the landslide identification problem.
- Landslide risk,
- Landslide identification,
- Machine learning,
- Deep learning,
- Big data,
- Convolutional neural networks

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References(36)

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