Skip to main content

​
resq_mcp.pdie.service

PDIE - Predictive Disaster Intelligence Engine. This module provides predictive disaster intelligence:
  • Vulnerability mapping for sectors (population, infrastructure, risks)
  • Probabilistic forecasts for disaster events
  • Pre-alert generation based on LSTM/GNN model outputs
The current implementation is stubbed with mock data for development.

​
annotations

​
random

​
uuid

​
Final

​
ErrorResponse

​
PreAlert

​
VulnerabilityMap

​
VULNERABILITY_DB

​
get_vulnerability_map

def get_vulnerability_map(sector_id: str) -> VulnerabilityMap | ErrorResponse
Retrieve precomputed vulnerability assessment for a sector. Part of PDIE (Predictive Disaster Intelligence Engine) system. Provides static infrastructure and risk data used as input to predictive models for disaster forecasting. Vulnerability Data Includes:
  • Population density classification (low/medium/high)
  • Critical infrastructure inventory (hospitals, bridges, etc.)
  • Flood risk score (0.0-1.0) from terrain and drainage analysis
  • Fire risk score (0.0-1.0) from fuel load and climate data
Arguments:
  • sector_id - Sector identifier (e.g., “Sector-1” through “Sector-4”).
Returns:
  • VulnerabilityMap - Comprehensive vulnerability data if sector exists.
  • ErrorResponse - Error message if sector_id is unknown.
Example:
vuln = get_vulnerability_map(“Sector-1”) if isinstance(vuln, VulnerabilityMap): … if vuln.fire_risk > 0.7: … print(f”High fire risk: {vuln.fire_risk}”) … print(f”Infrastructure: {vuln.critical_infrastructure}”)
Notes: Production systems would integrate with GIS databases and update vulnerability maps periodically based on infrastructure changes and seasonal risk factors.

​
get_predictive_alerts

def get_predictive_alerts(sector_id: str) -> list[PreAlert] | ErrorResponse
Generate probabilistic disaster forecasts for a sector. Part of PDIE system. Simulates the output of LSTM/GNN predictive models that analyze weather patterns, sensor trends, and historical data to forecast disasters before they occur. Prediction Logic (Simulated):
  • Checks vulnerability map for sector risk factors
  • Fire alert: Triggered if fire_risk > 0.5 (40% probability)
  • Probability: 0.75-0.95
  • Horizon: 4-24 hours
  • Flood alert: Triggered if flood_risk > 0.5 (40% probability)
  • Probability: 0.80-0.95
  • Horizon: 12-48 hours
  • Returns empty list if no alerts generated
Arguments:
  • sector_id - Sector identifier to generate forecasts for.
Returns:
  • list[PreAlert] - Zero or more pre-alerts with disaster forecasts if sector is valid.
  • ErrorResponse - Error message if sector_id is unknown.
Example:
alerts = get_predictive_alerts(“Sector-1”) if isinstance(alerts, list): … for alert in alerts: … print(f”Predicted: {alert.predicted_disaster_type}”) … print(f”Probability: {alert.probability:.0%}”) … print(f”Time horizon: {alert.forecast_horizon_hours}h”)
Integration Note: Production PDIE would run continuously with:
  • Weather API integration (NOAA, MeteoBlue)
  • IoT sensor stream processing (water levels, smoke detectors)
  • Historical incident database for pattern matching
  • LSTM models for time-series forecasting
  • GNN models for spatial correlation analysis