Characterizing Seasonality and Trend From In Situ Time‐Series Observations Using Explainable Deep Learning for Ground Deformation Forecasting

Abstract

Ground deformation, a critical indicator of geohazard evolution, exhibits both seasonal fluctuations and long‐term trend changes. This study explores interpretable deformation forecasting using an explainable deep learning approach, utilizing field observation data to extract and characterize these critical features from time series. We demonstrate that extracting and utilizing shared, similar seasonal and trend features across multi‐point observations significantly enhances ground deformation forecasting accuracy. Notably, the extracted seasonal features reveal the dominant role of the hydrological response in deformation at seasonal timescales, providing insights into the underlying source of predictability. This proof‐of‐concept study highlights the promise of interpretable ground deformation forecasting, with applications expected to encompass landslide behavior, volcanism, or seismic activity.