Enhancing Disaster Preparedness, Emergency Response and Resource Allocation using Big Data Integration and Predictive Analytic

Abstract

Natural disasters such as floods, hurricanes, and earthquakes can have devastating impacts on communities around the world. Improving disaster preparedness and emergency response is critical for saving lives and minimizing damage. This paper proposes leveraging big data integration and predictive analytics techniques to enhance disaster management capabilities. Multiple data sources including weather forecasts, geographic information systems, satellite imagery, social media, and census data are integrated to gain insights for predictive modeling. Statistical, machine learning, and deep learning methods are utilized to develop prediction models for disaster likelihood, severity, affected populations, required response resources, and optimal resource allocation strategies. Near real-time analytics on integrated data streams enables dynamic response optimization during disasters. Proposed techniques are validated on case studies of major natural disasters. Results demonstrate significant improvements in preparation, planning, resource mobilization, response coordination, and recovery efforts. Integrated big data analytics paves the way for more proactive and effective disaster management, potentially saving lives and minimizing losses.