A Multilevel AI-IoT Operational System for Landslide Early Warning: Transitioning from Heterogeneous Data Streams to Actionable Risk Intelligence

Recent decades have witnessed a marked increase in the frequency, intensity, and cascading disasters, resulting in severe social and economic losses, particularly when coupled with unpreparedness and social vulnerability. This contribution presents theoretical and applied advances in landslide disaster risk reduction, with emphasis on trigger analysis and the transformation of heterogeneous real-time data streams into actionable early warning intelligence. 

Amrita’s AI-Enabled Real-Time Landslide Early Warning System (A-LEWS) is designed for the real-time monitoring, detection, and early warning of landslides  (Ramesh, 2014). U.S. Patent No. 8,692,668). This system features Intelligent Wireless Probes (IWPs) equipped with various hydro-geophysical sensors, which are deployed deep beneath the earth’s surface to capture critical landslide-triggering parameters in vulnerable areas. The landslide detection system is founded on the integration of hydro-geophysical sensors that directly capture the physical processes governing rainfall-induced slope failure. Pore pressure transducers and dielectric soil-moisture sensors quantify rainfall infiltration, transient pore pressure buildup, and loss of effective stress, which are primary controls on slope instability (Figure 1). Tiltmeters and strain gauges measure slow ground deformation and changes in slope geometry associated with progressive failure, while geophones detect vibration signatures linked to material movement and subsurface fracturing. These heterogeneous sensors are interfaced through enhanced subsurface sensor columns and connected to wireless sensor nodes, enabling in situ, high-resolution monitoring across crown, middle, and toe regions of the slope. Given the constraints of remote deployments, limited power availability, difficult terrain, and long-term operation, the system adopts an energy-aware wireless sensor network design. Low-power operation is further supported by state-based node transitions, time synchronization, and selective high-rate sensing only during elevated-hazard conditions. Together, this sensor science and energy-efficient network architecture enable reliable, scalable, and long-duration landslide monitoring while preserving power resources without compromising early warning capability (Ramesh 2009).

Figure 1: Context-Aware IoT Edge Node Integrated with Adaptive Energy Management & Dynamic Sensor Prioritization 

A multilevel warning dissemination architecture ensures timely alerts to the relevant vulnerable community and stakeholders. This system in Munnar, Kerala, has been successfully providing warnings to the community since 2005, 2009, 2011, 2013, 2018, 2020, 2021, 2022, 2023, 2024, and 2025. A scalable version of LEWS has been implemented in Chandmari, Gangtok, Sikkim, where landslides are induced by both rainfall and earthquakes. Fully deployed in 2018, this system includes 11 IWPs with over 200 geophysical sensors. The system has been operational, with continuous monitoring, analysis, and reporting to the Sikkim State Disaster Management Authority (SSDMA). The effectiveness of the system in issuing successful warnings and supporting informed decision-making is illustrated in Figure 2.

 

Figure 2: Landslide Early Warnings Issued in 2020 for the Munnar area, Idukki, Kerala