Abstract
A real-time Internet of Things (IoT)-based warning system for slope safety monitoring was implemented at the Rumah Tinggi site in Singapore. This system combines advanced moisture sensors and osmotic tensiometers with cloud-integrated data acquisition for high-resolution monitoring of infiltration, soil water content, and pore-water pressures, enabling direct integration into real-time factor of safety (FoS) assessment. Two-dimensional seepage analysis was conducted to compare field sensor measurements with numerical results, ensuring robust model calibration. This study focused on the development of a linear regression model derived from regional water balance and slope stability analyses, which enables continuous, automated FoS estimation from in situ moisture sensor data. The primary objectives were to validate sensor-datalogger-cloud compatibility, evaluate sensor performance through numerical and experimental comparison, and demonstrate the feasibility of a scalable IoT framework for proactive slope stability assessment under varying climatic conditions. Field performance comparisons showed that capacitance moisture sensors provided more consistent and accurate measurements, aligning well with laboratory soil-water characteristic curves. The calculated factor of safety for the Rumah Tinggi testbed ranged between 3 and 5, with significant decreases during intense rainfall and slower recovery in dry periods. This IoT-based system offers a cost-effective solution with high-resolution data for geohazard monitoring and proactive risk management, significantly advancing landslide risk management strategies. Limitations include the need for further model validation under diverse conditions and the absence of direct quantitative comparison between rainfall intensity and modeled unit water flux.