An AI Driven Predictive Framework for Crisis Management and Organizational Resilience Using Multi Source Real Time Data

Abstract

Crisis situations demanding immediate and accurate decisions are naturally disruptive events like hurricanes, pandemics, cyber-attacks, or breakdowns of critical infrastructure. The usual crisis management systems that are heavily reliant on manual operations and out-dated information become less effective in the response and recovery phases. So, this paper puts forward a hybrid strategy which entails both the examination of management of crisis literature and the development of an AI-driven predictive framework. The system envisaged in the paper employs machine learning models along with multi-source real-time data to identify crisis at its earliest stage, calculate the crisis level, and support resilience planning. The framework achieves a better understanding of the environment by utilizing data from the surroundings, readings from the sensor, the analysis of social media, and historical records of the events. A tiny experiment with some sample datasets was employed to help the approach's potential. The findings suggest that the prediction accuracy has been enhanced, and the response time has been shortened. The article is a significant step towards the development of a comprehensive framework for AI-driven crisis prediction and resilience planning that can be adopted by governments, industries, and emergency response organizations.