IoT-Enabled Real-Time Environmental Monitoring and Disaster Prediction System

Abstract

Environmental disasters floods, wildfires, landslides, earthquakes, and extreme air quality events collectively claim over 70,000 lives and inflict $200 billion in economic damage annually. Timely, accurate warning systems can dramatically reduce these tolls; yet most existing approaches rely on sparse, manually operated sensor networks with limited predictive capability. This paper presents a comprehensive IoT-enabled real-time environmental monitoring and disaster prediction system that integrates a heterogeneous wireless sensor network (WSN), a three-tier edge-cloud processing architecture, and a deep learning-based predictive engine. Eight categories of environmental sensors stream data through MQTT and LoRaWAN protocols to edge computing nodes (Raspberry Pi 4 and NVIDIA Jetson Nano) for preprocessing, anomaly detection, and local inference, with refined data forwarded to a cloud platform running Apache Kafka, InfluxDB, and a trained LSTM prediction model. Evaluated across six disaster categories on a six-month real-world deployment in the Western Ghats region of Maharashtra, India, the proposed system achieves 96.4% prediction accuracy with a mean lead time of 72 minutes before disaster onset. MQTT end-to-end latency is 12 ms on average, while energy consumption from the cloud is reduced by 34% using edge-offloading technique compared to the cloud-based approach alone. This study shows that scalable and low-latency IoT frameworks together with temporal deep learning make an effective early warning platform for smart cities and disaster zones.