SOCIAL MEDIA–DRIVEN FLOOD CRISIS MANAGEMENT: AN AI APPROACH USING NLP AND MACHINE LEARNING FOR REAL-TIME DISASTER DETECTION AND RESPONSE

Floods are among the most destructive natural disasters, causing significant economic loss,
infrastructure damage, and human casualties. Traditional flood monitoring and response systems rely
heavily on sensor networks, satellite imagery, and official reports, which often have limitations in
coverage, timeliness, and granularity. Meanwhile, social media platforms provide vast amounts of real-
time, user-generated data, offering an opportunity to enhance situational awareness and disaster
response. This paper proposes an AI-driven framework for flood crisis management that integrates
Natural Language Processing (NLP) and Machine Learning (ML) to process social media data for
real-time detection, severity classification, and response prioritization. The proposed system includes a
data collection module for social media streams, pre-processing and feature extraction using NLP
techniques, ML-based classification of flood events, and a decision-support layer for emergency response.
Simulated experiments demonstrate the feasibility of this approach, showing high accuracy in flood
detection and severity estimation. This framework provides a scalable and timely tool for authorities
and disaster management agencies to improve flood preparedness and response.