Solar-Powered IoT-Based Landslide Early Warning System for Remote and High-Risk Areas

Abstract

Landslides pose a significant threat to human safety and critical infrastructure, particularly in remote and high-rainfall regions. In these areas, continuous monitoring is difficult to sustain and reliable access to power is often limited. This paper presents the development and experimental evaluation of a solar-powered landslide early warning system (S-LEWS) that combines soil moisture and ground vibration sensing with Internet of Things (IoT) technology. An ESP32 microcontroller is employed for real-time data acquisition and slope condition assessment, classifying risk levels into safe (S < 40%), warning (40% ≤ S < 70%), and danger (S ≥ 70% or vibration detected) states using predefined threshold criteria. Simulation and prototype-based testing confirmed accurate and repeatable alert responses across all evaluated scenarios, with ground vibration detection prioritized to ensure immediate danger alerts regardless of soil moisture conditions. The system provides hybrid sensing warning outputs, including visual indicators, an audible alarm and wireless notifications via Telegram. Energy autonomous operation is achieved through a solar photovoltaic (PV) module integrated with battery storage, enabling continuous day-and-night functionality without reliance on grid power. The findings demonstrate that S-LEWS operates reliably and show strong potential for sustained landslide risk monitoring in resource-constrained environments.