4.7 Article

AI-powered landslide susceptibility assessment in Hong Kong

Journal

ENGINEERING GEOLOGY
Volume 288, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.enggeo.2021.106103

Keywords

Landslide susceptibility; Landslide risk; Machine learning; Convolutional neural network (CNN); Bidirectional long short-term memory (BiLSTM); Convolutional neural network long short-term memory (CNN-LSTM)

Funding

  1. Research Grants Council of the Hong Kong SAR Government [16205719, AoE/E-603/18, 16203720]

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The study introduces a novel AI-powered object-based landslide susceptibility assessment method that defines landslide and non-landslide objects based on historical statistics, constructs samples, and utilizes AI techniques for prediction. A comprehensive case study in Hong Kong demonstrates the superiority of this method over traditional approaches and produces territory-wide landslide susceptibility maps using AI algorithms.
Landslide susceptibility assessment is essential for regional landslide risk assessment and mitigation. Most past studies involved cell-based analysis that takes landslide incidents as geo-spatial points. Nevertheless, given that a landslide is a two-dimensional polygon on maps and a three-dimensional object in the real world, an object-wise assessment is more logical. Fusing with artificial intelligence (AI) techniques, this paper proposes a novel AI-powered object-based landslide susceptibility assessment method to address this issue. First, landslide and non-landslide objects are defined based on an optimal object size determined by statistics of historical landslides. Next, landslide and non-landslide samples are constructed by integrating geoenvironmental data layers derived from multi-source data. Subsequently, AI techniques are applied to learn susceptibility prediction based on the prepared samples. To illustrate the proposed method, a comprehensive case study of Hong Kong is conducted, in which six AI algorithms are evaluated including logistic regression (area under curve, AUC = 0.949), random forest (AUC = 0.951), LogitBoost (AUC = 0.958), convolutional neural network (CNN) (AUC = 0.966), bidirectional long short-term memory architecture of recurrent neural network (BiLSTM-RNN) (AUC = 0.966), and CNN-LSTM (AUC = 0.972), among which the BiLSTM-RNN and CNN-LSTM algorithms are applied in landslide susceptibility assessment for the first time. Results confirm that the proposed object-based method outperforms the traditional cell-based method significantly. Equally importantly, the case study produced the first set of AI-based territory-wide landslide susceptibility maps for Hong Kong. These maps can be used as a fundamental tool for quantifying natural terrain landslide risk and identifying susceptible zones where landslide mitigation measures may be needed.

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