AI & HYDROLOGICAL MODELING SPECIFICATIONS
Risk Calculation Methodology & AI Models
Detailed technical breakdown of our composite flood risk scoring algorithm, RAG vector retrieval, and confidence metrics.
Official Sources: India Meteorological Department (IMD), Central Water Commission (CWC), KSDMA
Updated: 07:23 pm IST
Next Sync: 07:38 pm
Algorithmic Spec v2.4Data Provenance & Specs
1. Composite Risk Score Index (0 - 100%)
The composite risk score is evaluated dynamically across each district using a weighted mathematical model:
RiskScore = (0.35 × R_24h) + (0.25 × W_river) + (0.25 × S_dam) + (0.15 × L_soil)
- R_24h: Normalized 24-hour rainfall anomaly index relative to IMD extreme precipitation baselines.
- W_river: Highest river gauge level ratio compared to CWC danger thresholds across district river basins.
- S_dam: Reservoir storage fullness percentage and active shutter discharge volume.
- L_soil: Soil moisture saturation percentage from satellite microwave telemetry.
2. Confidence Interval & Telemetry Quality Assurance
Every calculated risk score is tagged with a confidence metric (High, Medium, Moderate). Scores updated within 15 minutes of official IMD/CWC bulletins carry a 98% confidence rating. In cases of sensor telemetry latency, historical trend extrapolation is applied and flagged transparently.