Using volumetric water content measurements with the implementation of machine learning for monitoring shallow landslides induced by rainfall

Improved and affordable prediction techniques are required because the growing frequency of shallow landslides caused by shifting weather patterns poses severe dangers to ecosystems, infrastructure, and communities. Although comprehensive monitoring systems are available, their high costs and complexity often make them impractical in resource-constrained regions. This study aims to evaluate the predictive potential of volumetric water content (VWC) measurements for shallow landslides and leverage machine learning techniques to develop cost-effective prediction models. The study employed one-dimensional modified column tests to simulate various scenarios (e.g., soil densities, drainage conditions) using a one-meter-high acrylic column to measure VWC, pore water, and air pressure. Key findings include the identification of VWC-related parameters (e.g., steady-state VWC and its gradient) as effective predictors of slope failure. When integrated with ML models, these parameters demonstrate the potential for enhancing prediction accuracy. This study provides a pathway to developing cost-effective early warning systems for slope instability, offering a practical solution for improving safety, using volumetric water content measurements to protect infrastructure, and enhancing resilience in landslide-prone regions, mainly where comprehensive monitoring systems are infeasible.