Dynamic landslide susceptibility mapping over recent three decades to
  uncover variations in landslide causes in subtropical urban mountainous areas
Peifeng Ma Li Chen Chang Yu Qing Zhu Yulin Ding
Landslide susceptibility assessment (LSA) is of paramount importance in
mitigating landslide risks. Recently, there has been a surge in the utilization
of data-driven methods for predicting landslide susceptibility due to the
growing availability of aerial and satellite data. Nonetheless, the rapid
oscillations within the landslide-inducing environment (LIE), primarily due to
significant changes in external triggers such as rainfall, pose difficulties
for contemporary data-driven LSA methodologies to accommodate LIEs over diverse
timespans. This study presents dynamic landslide susceptibility mapping that
simply employs multiple predictive models for annual LSA. In practice, this
will inevitably encounter small sample problems due to the limited number of
landslide samples in certain years. Another concern arises owing to the
majority of the existing LSA approaches train black-box models to fit distinct
datasets, yet often failing in generalization and providing comprehensive
explanations concerning the interactions between input features and
predictions. Accordingly, we proposed to meta-learn representations with fast
adaptation ability using a few samples and gradient updates; and apply SHAP for
each model interpretation and landslide feature permutation. Additionally, we
applied MT-InSAR for LSA result enhancement and validation. The chosen study
area is Lantau Island, Hong Kong, where we conducted a comprehensive dynamic
LSA spanning from 1992 to 2019. The model interpretation results demonstrate
that the primary factors responsible for triggering landslides in Lantau Island
are terrain slope and extreme rainfall. The results also indicate that the
variation in landslide causes can be primarily attributed to extreme rainfall
events, which result from global climate change, and the implementation of the
Landslip Prevention and Mitigation Programme (LPMitP) by the Hong Kong
government.