This paper presents the development of a low-cost, highly accurate, and proactive landslide prediction and early warning system designed for high-risk regions such as the Western Ghats in Kerala, India. Existing monitoring frameworks primarily rely on reactive approaches that fail to provide sufficient warning time before catastrophic slope failures. To address this, the proposed system integrates Internet of Things (IoT) sensor nodes, edge computing, cloud-based Long Short-Term Memory (LSTM) neural networks, and physics-based Digital Twin simulations into a unified architecture. By combining real-time ground data, such as vibration, tilt, and soil moisture, with Factor of Safety (FoS) calculations, the framework delivers reliable early warnings up to 72 hours before potential events. The integration of multi-channel alerting mechanisms ensures timely dissemination to authorities and local communities. This research demonstrates that synergizing edge-level intelligence with cloud-based deep learning bridges the critical gap between theoretical disaster prediction and practical, life-saving implementation.

This paper presented an integrated AI-IoT framework for the real-time detection and early warning of landslides and floods. By shifting from traditional reactive monitoring to a proactive predictive model, the system successfully bridges the gap between advanced deep learning research and practical, cost-effective disaster management. The fusion of LSTM neural networks with physics-based Digital Twin Factor of Safety calculations provides a robust, dual-validation mechanism that significantly enhances prediction reliability. Future work will focus on the pilot deployment of 3-5 sensor nodes in specific Wayanad catchment areas to gather real-world performance metrics and further train the predictive models on hyper-local geological data.

1.Collaborative effort by IIM Kozhikode, NIT Calicut, IIT Bombay, and Keio University Japan,”Wayanad Landslides 2024: Early Warning System: Changing the last mile to the first mile”,2025. 2.A. A. Mohammed et al.,”A Survey On Real-Time Flood Monitoring And Alert System Using IoT”,2023. 3.R. Kumar, S. Sharma, and P. Verma,”Landslide Detection and Alert System using Wireless Sensor Network”,2024. 4.L. Wang, X. Zhang, and Y. Chen, “Stability Prediction of Soil Slopes Based on Digital Twinning and Deep Learning”,2023. Z. Liu, H. Yang, and W. Li,”Multi-Scale Response Analysis and Displacement Prediction of Landslides Using Deep Learning with JTFA: A Case Study in the Three Gorges Reservoir, China”,2023.

© 2025 International Journal of Engineering and Techniques (IJET).

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