Reshaping Disaster Resilience

Abstract

Natural disasters, such as earthquakes, hurricanes, floods, and wildfires, are ongoing worldwide concerns that have catastrophic effects on human lives, infrastructure, and the environment. By enhancing forecast, management, and reaction tactics, machine learning and artificial intelligence (AI) have emerged as revolutionary technologies in solving these crises. This comprehensive chapter delves deeply into the uses and implications of machine learning and AI in natural disaster avoidance. Machine learning techniques, especially artificial neural networks (ANNs), have shown promise in forecasting the incidence and severity of many natural catastrophes. These models make use of massive datasets including climatic, geographical, and historical data to improve forecasting accuracy and early warning systems. Furthermore, data-driven insights enable catastrophe prediction and risk assessment using a variety of machine learning methods ranging from decision trees to deep learning networks.