Assessing the impact of rainfall memory on landslide susceptibility using binary time encoding

Landslides are among the most destructive natural hazards in mountainous regions. Their occurrence is jointly governed by predisposing factors such as topography, geology, and soil properties, as well as external triggers such as rainfall. The temporal evolution of rainfall plays a crucial role in controlling pore-water pressure build-up and slope instability. However, most existing data-driven studies rely on metrics that condense complex information into scalar quantities – such as accumulated precipitation or maximum intensity – that fail to capture the “memory effect” of antecedent rainfall and wet–dry cycles on slope stability. This leaves an important question unresolved: how do the accumulation and temporal patterns of historical rainfall across different time scales influence the likelihood that a subsequent rainfall event will trigger landslides?

To address this problem, we propose a binary time-encoding approach for long- and short-term rainfall sequences. The method transforms continuous rainfall records into binary indicators that describe the occurrence, persistence, and temporal arrangement of rainfall. By summarizing rainfall history across multiple time windows, the approach preserves key antecedent information while reducing noise in long rainfall series and substantially lowering computational cost, making it suitable for large-scale, multi-event landslide susceptibility and spatio-temporal forecasting models.

We designed case studies using open-access landslide inventories, such as Northeastern Turkey, Italy, Switzerland, and precipitation datasets to compare (i) models built with conventional cumulative or intensity-based rainfall metrics and (ii) models incorporating the proposed binary time-encoded rainfall features. The analysis is implemented within the SHIRE framework (Edrich et al., 2024), while introducing a novel binary time-encoding strategy for long- and short-term rainfall sequences. Here, we present results demonstrating how antecedent rainfall at different temporal scales influences landslide occurrence and show that binary time encoding provides a compact and transferable representation of rainfall “memory” for regional landslide hazard assessment and early-warning frameworks.

References
Edrich, AK., Yildiz, A., Roscher, R., Bast, A., Graf, F. & Kowalski, J., A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning. Natural Hazards 120, 8953–8982 (2024). https://doi.org/10.1007/s11069-024-06563-8