Abstract

The increasing frequency and intensity of natural disasters necessitates innovative approaches to enhance disaster resistance and recovery. This study explores the integration of Big Data and Artificial Intelligence as trans-formative tools in disaster management. For that I propose a multi-faceted methodology that leverage real-time data analytics, predictive modeling and machine learning algorithm to improved preparedness, response, and recovery efforts. The approach utilizes diverse data sources, including satellite imagery, social media feeds, weather patterns and historical incident reports, to create a comprehensive understanding of disaster dynamics. We implement on AI-driven framework that identifies vulnerability hotpots, predicts potential disaster impacts and optimizes resource allocation during recovery phases.
Through case studies, I demonstrate how this methodology can enhance decision-making for emergency res-ponders. Improve communication strategies with affected communities, faster resilience infrastructure Department. The findings suggest that the adoption of these technologies not only facilitate timely intervention and resource management but also fasters community engagement and awareness.
The following method can be used for better result on Leveraging Big Data and AI for Disaster Resilience and Recovery. That’s are- (1) Data Collection: Aggregating data from multiple sources, (2) Data processing and Integration- Utilizing data fusion technologies to combine heterogeneous data types into a unified framework. (3) predictive modeling-Implementing machine learning algorithms (e.g.-random forest, Neural Networks) (4) Real time. Analytics (5) Resource optimization. (6) Post Disaster-Evaluation (7) Community engagement and education (8) Continuous improvement loop.
By adopting data driven method framework, organization and governments can enhance disaster resilience and recovery efforts The seamless integration of Big Data and AI facilitate proactive risk management, informed decision-making and ultimately, better outcomes for affected communities. Effective implementation of this method hinges on continuous data quality improvement, stakeholder collaboration, and adaptability to evolving technological landscapes.