Data Science for Disaster Resilience: Predictive Modeling and Response Optimization

Abstract

Disaster resilience requires proactive planning and efficient response strategies, both of which can be enhanced by data science. This paper examines how predictive modeling, real-time data analytics, and geospatial analysis can improve disaster preparedness and response. By integrating data science with emergency management, environmental science, and social policy, we explore the potential of these technologies to mitigate the impact of natural disasters such as bushfires, floods, and cyclones. Case studies from Australia highlight the challenges and opportunities of implementing data science in disaster management. The paper concludes with recommendations for fostering collaboration between data scientists, emergency responders, and policymakers to build resilient communities.