Harnessing LoRa for real-time landslide monitoring and early alerts in Kerala’s terrain

• Cost-effective sensor network for real-time landslide monitoring. • LoRaWAN and machine learning (ML) improve prediction and response. • Multi-sensor suite monitors soil, motion, and environmental factors. • On-site ML enables rapid detection and reduces data load. • Kerala field tests link alerts to the National Landslide Susceptibility Mapping division, Geological Survey of India. Landslides trigger high loss of life, damage to property and infrastructure, particularly in sensitive terrain areas like Kerala, India. Real-time monitoring and forecasting remain difficult due to rugged topography and low connectivity in remote terrain. The current work depicts a low-power, long-range IoT framework for monitoring applications utilizing LoRaWAN for data transmission and machine learning for forecasting. Soil moisture, accelerometer–gyroscope (MPU6050), humidity (DHT22), and simulated piezometer sensor nodes periodically store important slope-stability parameters. The sensed data are transmitted across LoRa to a base hub where the site-specific machine learning program analyzes the data in real time. Experimental results reveal soil moisture increasing from 2 % to 10 %, humidity from 89.8 % to 91.5 %, pore water pressure from 0.2 kPa to 0.5 kPa, and fluctuating accelerometer during simulated slope failure—variables closely related to landslide initiating factors. Machine learning outcomes reveal the ExtraTrees Classifier obtained 87.0 % accuracy and gave the best results relative to different algorithms. The system provides automatic SOS messages to the Geological Survey of India (GSI) and executes site-based alarms for communities at risk. In comparison with the current GSM or satellite-based systems, the presented method provides longer-range communications and reduced energy consumption, along with quicker responses. The work presents a field-applicable and scalable solution for landslide risk management and disaster preparedness applications.