A Scalable Hydrology-Informed Monitoring System for Early Detection of Slope Failures Using IoT and ML

Landslides pose significant risks to infrastructure, the environment, and human life, particularly when triggered by intense or prolonged rainfall. Accurate and timely prediction of slope failures remains challenging due to the complex interactions between hydrology and geotechnics, as well as spatial variability in soil conditions. This paper presents a hydrology-informed early-warning system that integrates Machine Learning (ML) and Internet of Things (IoT) technologies for real-time landslide monitoring and short-term forecasting. The proposed system is demonstrated using a laboratory-scale slope model designed as a modular monitoring unit that can be replicated across multiple locations for regional deployment. The system is trained and validated using both real-time sensor data collected from controlled laboratory rainfall experiments and synthetic hydrological datasets generated through artificial rainfall simulations that emulate large-scale, failure-prone slope conditions. Multiple ML models were developed using slope tilt measurements, soil matric suction, and soil moisture data to predict slope instability. A web-based graphical user interface enables remote, real-time visualization of sensor data. It provides automated alerts while supporting forecasts of slope failure one, two, and four days in advance. Experimental results indicate that the proposed system effectively detects slope movements and forecasts failures with acceptable accuracy, low computational overhead (approximately 148ms per sensor transmission cycle), and near real-time end-to-end latency from data acquisition to visualization. Among the evaluated models, the random forest model consistently achieved superior performance. This work establishes a validated proof of concept under controlled laboratory conditions, with future field validation on natural slopes identified as a critical next step. In addition, a cost analysis for a prospective large-scale deployment in the Greater Houston Area demonstrates the economic feasibility of the proposed monitoring architecture.