Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency

Abstract

Landslide susceptibility is crucial for assessing the probability and severity of landslide disasters in a region. Previous studies have focused on static landslide susceptibility, using landslide assessment factor data from varying years, making it difficult to estimate spatio-temporal consistency and resulting in low prediction accuracy. Taking Hong Kong, China, as the study region, this study proposes a framework to estimate spatio-temporally consistent landslide susceptibility. The landslide assessment factors are divided into static and dynamic factors, with a temporal resolution of 5 years. Specifically, the dynamic assessment of landslide susceptibility is conducted for the periods 2000–2004, 2005–2009, 2010–2014, and 2015–2019, covering a total span from 2000 to 2019. Results show that the accuracy of the proposed model, defined as the proportion of correctly classified samples relative to the total number of samples, exceeds 0.7 across these four time periods. Both the F1-Score and the receiver operating characteristic (ROC) curve indicate that the proposed research framework exhibits good accuracy and practicality in susceptibility assessment. The proposed framework could capture temporal variations in landslide occurrence, allowing for a more accurate prediction of landslide susceptibility. The findings provide valuable insights for landslide disaster prevention and mitigation in Hong Kong and would also be applicable in other countries or regions.

Keywords

• landslide
• susceptibility
• machine learning
• spatio-temporal data