Signals from the Floods: AI-Driven Disaster Analysis through Multi-Source Data Fusion

TL;DR

This paper presents an AI-driven approach that fuses social media data and public inquiry submissions to enhance disaster analysis, improve situational awareness, and support emergency response during floods.

Contribution

It introduces a novel multi-source data fusion method combining LDA and LLMs to filter and analyze flood-related data for better disaster response.

Findings

Relevance Index effectively filters flood-relevant social media content.

Combined data streams reveal behavioral patterns and geographic opinions.

Enhanced situational awareness aids emergency responders.

Abstract

Massive and diverse web data are increasingly vital for government disaster response, as demonstrated by the 2022 floods in New South Wales (NSW), Australia. This study examines how X (formerly Twitter) and public inquiry submissions provide insights into public behaviour during crises. We analyse more than 55,000 flood-related tweets and 1,450 submissions to identify behavioural patterns during extreme weather events. While social media posts are short and fragmented, inquiry submissions are detailed, multi-page documents offering structured insights. Our methodology integrates Latent Dirichlet Allocation (LDA) for topic modelling with Large Language Models (LLMs) to enhance semantic understanding. LDA reveals distinct opinions and geographic patterns, while LLMs improve filtering by identifying flood-relevant tweets using public submissions as a reference. This Relevance Index method reduces noise and prioritizes actionable content, improving situational awareness for emergency responders. By combining these complementary data streams, our approach introduces a novel AI-driven method to refine crisis-related social media content, improve real-time disaster response, and inform long-term resilience planning.