Knowledge-infused Deep Learning Enables Interpretable Landslide
  Forecasting
Zhengjing Ma Gang Mei
Forecasting how landslides will evolve over time or whether they will fail is
a challenging task due to a variety of factors, both internal and external.
Despite their considerable potential to address these challenges, deep learning
techniques lack interpretability, undermining the credibility of the forecasts
they produce. The recent development of transformer-based deep learning offers
untapped possibilities for forecasting landslides with unprecedented
interpretability and nonlinear feature learning capabilities. Here, we present
a deep learning pipeline that is capable of predicting landslide behavior
holistically, which employs a transformer-based network called LFIT to learn
complex nonlinear relationships from prior knowledge and multiple source data,
identifying the most relevant variables, and demonstrating a comprehensive
understanding of landslide evolution and temporal patterns. By integrating
prior knowledge, we provide improvement in holistic landslide forecasting,
enabling us to capture diverse responses to various influencing factors in
different local landslide areas. Using deformation observations as proxies for
measuring the kinetics of landslides, we validate our approach by training
models to forecast reservoir landslides in the Three Gorges Reservoir and
creeping landslides on the Tibetan Plateau. When prior knowledge is
incorporated, we show that interpretable landslide forecasting effectively
identifies influential factors across various landslides. It further elucidates
how local areas respond to these factors, making landslide behavior and trends
more interpretable and predictable. The findings from this study will
contribute to understanding landslide behavior in a new way and make the
proposed approach applicable to other complex disasters influenced by internal
and external factors in the future.