Sustainable Emergency Response through Data-Driven and Machine Learning Approaches: A Review

The study highlights the increasing occurrence of catastrophic events stemming from rapid population growth, urbanization, and global climate change. It emphasizes the need for innovative approaches to decision-making in uncertain situations, considering globalization and technological advancements. Emergency response personnel can harness data from critical infrastructure, smartphones, and social media to gain insights, anticipate affected populations, and formulate comprehensive action plans using big data. Big data analytics provide real-time indicators for on-site disaster statistics, enabling swift feedback loops and precise assessments. The Dynamic Data-Driven Application Systems (DDDAS) framework, exemplified by “I Revive,” is explored for making critical decisions in emergency medical care. This dynamic environment continuously receives and responds to real-time data, facilitating global collaboration for synchronized decision-making.