V3 Final Release

A Neuro-Symbolic Disaster
Intelligence Engine.

Combines XGBoost impact regression with pgvector semantic search over 2,281 historical disaster reports to simulate affected population and economic damage for any hazard event, anywhere in the world.

Launch Simulator →
View on GitHub
25 Yearsof global disaster data (2000–2025)
2,281situation report narratives in vector DB
1,024embedding dimensions (BGE-Large)
5+hazard classes with domain-specific models
4-sourcefusion: USGS · NASA · EM-DAT · Smithsonian

How it works

01

Configure the Event

Select a country, hazard class (earthquake, flood, etc.), month, year, and severity on the Richter or Saffir–Simpson scale.

02

Run the Math Engine

XGBoost regression models — trained separately for each disaster type — predict estimated affected population and economic impact in USD.

03

Retrieve Historical Context

pgvector performs a cosine similarity search over 2,281 embedded disaster narratives to surface the 3 most analogous historical events by meaning.

Built with

Python 3.11XGBoostPostgreSQLpgvectorFastAPIsentence-transformersBGE-Large-en-v1.5Next.js 16MapLibre GLFramer Motion