Machine Learning Algorithms for Natural Disasters

Abstract

Natural disasters require quick and precise reactions for preparedness, mitigation, and response activities because they pose serious risks to infrastructure, human lives, and the environment. The incorporation of machine learning (ML) algorithms has become a viable strategy to improve natural disaster management in a number of ways in recent years. Early warning systems and risk assessment frameworks are made possible by predictive models that are able to identify patterns, anomalies, and risk factors from a variety of data sources thanks to techniques like supervised learning, unsupervised learning, and deep learning. The application of machine learning algorithms to natural disaster management poses a number of issues and concerns, notwithstanding its potential advantages. By combining various data sources, sophisticated analytics, and real-time decision support systems, machine learning (ML) algorithms enable stakeholders to more effectively and resiliently prepare for, mitigate, and respond to natural catastrophes.