AI-Driven LoRaWAN-Enabled Solar-Powered Multi-Sensor Framework for Proactive Flood Prediction and Early Warning

Floods remain one of the most devastating natural disasters, causing significant loss of life, infrastructure damage, and environmental degradation, particularly in remote and resource-constrained regions. Conventional flood monitoring systems are largely reactive, rely on limited sensing parameters, and often fail during extreme weather due to power and communication constraints. This paper presents an AI-driven, LoRaWAN-enabled, solar-powered multi-sensor framework designed for proactive flood prediction and early warning. The proposed system integrates rainfall, water level, flow rate, atmospheric pressure, temperature, and humidity sensors with an embedded controller for real-time environmental monitoring. Sensor data are transmitted over long distances using low-power LoRaWAN communication and uploaded to a cloud platform via an IoT gateway. Python-based machine learning models analyze historical and real-time data to predict flood risk levels in advance. Alert mechanisms include GSM-based SMS notifications and local alarms through visual and audio indicators. Solar energy with battery backup ensures uninterrupted operation during power outages. Experimental results demonstrate reliable long-range communication, continuous monitoring, and timely alert generation, making the proposed framework suitable for deployment in flood-prone and remote regions to enhance disaster preparedness and response.