Adaptive AI-Lora Early-Warning System for Remote Disaster-Prone Areas
DOI:
https://doi.org/10.15662/IJEETR.2026.0802093Keywords:
Disaster management, LoRa communication, Internet of Things, Early warning system, Artificial intelligence, Remote monitoringAbstract
Remote and disaster-prone regions frequently experience failures in conventional communication infrastructure during emergency situations, resulting in delayed warnings and increased loss of life and property. To address this challenge, this paper presents an adaptive AI-driven LoRa-based early-warning system designed to operate reliably in infrastructure-deficient environments. The proposed system employs distributed sensor nodes to continuously monitor critical environmental parameters, including flood level, rainfall, soil moisture, seismic vibration, fire, and gas leakage. Upon detecting abnormal conditions, sensor data is transmitted using a low-power long-range (LoRa) communication network to a central gateway. An embedded AI-based decision module processes multi-sensor data to classify disaster type and severity and generates context-aware safety instructions rather than generic alerts. The system operates on battery power with optional solar support, ensuring continuous functionality during power outages. Experimental evaluation demonstrates reliable long-range communication, low power consumption, and timely generation of actionable alerts. The proposed solution is suitable for large-scale deployment in rural and remote regions for effective disaster management and early warning
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