A Machine Learning and IoT-Driven Framework for Real-Time Disaster Management

Natural calamities are perilously challenging for people and communities that are already vulnerable. The traditional forecasting methods lack precision and would be way behind in terms of adaptability due to ever-changing climatic conditions. This chapter considers a model where machine learning-based IoT-enabled weather station networks are combined for disaster forecasting and management of floods, earthquakes, cyclones, droughts, wildfires, and extreme heat. Informer model in its core form, which is a very effective transformer-based deep learning model, has got a maximum test accuracy of 91.22% in severe weather prediction. This is already implemented in Rajshahi to improve disaster readiness and response at real time and recovery. The solution is scalable and eco-friendly and beats the traditional ones in efficiency while ensuring the livelihoods with AI and IoT that would reduce agricultural losses and socio-economic impacts.