Dynamic landslide susceptibility mapping over last three decades to uncover variations in landslide causation in subtropical urban mountainous areas

Landslide susceptibility assessment (LSA) plays a vital role in disaster prevention and mitigation. Recently, numerous data-driven LSA approaches have emerged. Nonetheless, most of them neglected the rapid oscillations within the landslide-prone environment, primarily due to significant changes in external triggers such as rainfall, which would render landslides susceptible to varying causations over time. Thus, conducting dynamic landslide susceptibility mapping (D-LSM) and revealing the underlying trends in landslide causes, become increasingly important for effective landslide hazard assessment. This study decomposed the entire D-LSM task into yearly LSA subtasks, and innovatively meta-learned intermediate representations that can be well-generalized and fine-tuned in a fast-adaptation manner. Then, to interpret the model predictions and characterize the variations in landslide causation, Shapley Additive exPlanations (SHAP) was utilized for feature permutation year by year. In addition, MT-InSAR techniques were applied to enhance and validate the D-LSM results. The study area was Lantau Island, Hong Kong, where the yearly LSA was executed from 1992 to 2019. The performance comparison results show that the proposed method outperformed the other approaches with regard to accuracy (3 %–7 %), precision (2 %–9 %), recall (3 %–5 %), and F1-score (2 %–7 %), even when adopting a fast adaptation strategy using only 5 samples and 5 gradient descent updates. This validates the applicability of meta-learning for identifying commonalities across multi-temporal LSA tasks. The overall model interpretation results indicate that slope and extreme rainfall were the primary contributors to landslide occurrences in Hong Kong. The feature permutation results over the 30 years reveal a variation in landslide causation, particularly a dramatic shift in the ranking of some contributing factors under extreme weather conditions. Remarkably, the importance of AERD (Annual Extreme Rainfall Days), a factor indicating extreme rainfall intensity, was deeply affected by global climate change and the government's Landslide Prevention and Mitigation Programme (LPMitP).