Scientific Abstract & System Specification
Disaster mitigation across vulnerable Indian sub-regions requires high-fidelity predictive modeling combining historical geophysical disaster catalogues with historical meteorological telemetry. RESQ implements a medallion architecture (Bronze/Silver/Gold) ingesting multi-hazard records (cyclones, earthquakes, landslides, MODIS satellite fire data) coupled with 11-year Open-Meteo climate records to power dual-output XGBoost classifiers predicting incident type and hazard severity.
Core Architectural Challenge
Multi-disaster risk modeling ETL pipeline and dual-output XGBoost classification engine for cyclones, earthquakes, landslides, and floods across India.
Mathematical Basis & Formal Invariants
Medallion ETL transformations, spatio-temporal lag convolutions, dual-output gradient-boosted decision trees (XGBoost).
Key Empirical Findings & Benchmark Metrics
[01]
Automated medallion data pipeline (Bronze-to-Gold) normalizing multi-source disaster records.
[02]
Dual-output XGBoost classification predicting hazard type and severity with localized weather enrichment.
[03]
Integrated rescue coordination directory linking relief NGOs across 6 high-risk Indian states.
Verification & Telemetry Testbed
Open-Meteo Climate Telemetry & MODIS Satellite Disaster Datasets
Formal Verification & Engineering Stack
Ayaan SaifiMatrika RegmiQayad AliMehra FatmaAlisha KhanNashra ZehraKhalid Mohammad ZakiYahya Farooq