Crowdsourced Disaster Management

Abstract

This research introduces a comprehensive crowdsourced disaster management system utilizing artificial intelligence to enhance real-time response, decision-making, and disaster mitigation. The system integrates deep learning models for disaster detection, categorization, and prediction, leveraging cloud-based AWS services for scalability, reliability, and accessibility. The methodology includes real-time data gathering from social media platforms, IoT sensors, governmental databases, and user-generated reports, ensuring a robust and multi-source approach for situational awareness. By actively involving community participation through mobile and web-based applications, the system strengthens resilience and ensures immediate response to emergency situations. The project addresses critical challenges such as misinformation filtering, automatic classification of disaster severity, automated response recommendations, and infrastructure scalability. With advancements in AI-driven data analytics, the platform ensures efficient disaster response by optimizing resource allocation, reducing response time, and improving the coordination between emergency services and affected populations. The paper highlights the transformative potential of AI in disaster preparedness, mitigation, and response through intelligent automation and crowdsourced intelligence.