IoT and ML for Predictive Disaster Response and Environmental Monitoring
Abstract
Rising natural calamities demand intelligent disaster management beyond traditional reactive methods. IoT-ML integration revolutionizes this by enabling real-time environmental monitoring via sensors, drones, and satellites tracking temperature, humidity, air quality, seismic activity, and water levels. ML algorithms—decision trees, CNNs, RNNs—analyze data streams for anomaly detection, disaster forecasting, and early warnings, reducing fatalities and economic losses from earthquakes, floods, hurricanes, and wildfires. Post-disaster, AI assesses damage from imagery for efficient resource allocation. Challenges like data accuracy, cybersecurity, interoperability, and computation persist, addressable by edge computing, federated learning, and blockchain for scalable, resilient systems.
