๐Ÿ“ก SENSOR TELEMETRY: ZONE: Active Location ACCUMULATION: 10.5 mm INUNDATION RISK: LOW SATELLITE: INSAT-3DR Operational STATUS: Normal Telemetry
EARLY WARNING OPERATIONS PLATFORM

Command Center

SYSTEM OPERATIONAL
INTEGRATED EARLY WARNING

Predict the rain. Map the risk. Act before inundation.

Multi-source architecture combining satellite, radar, observations, NWP and terrain data for location-specific disaster intelligence. Real-time multi-source data fusion and machine-learning rainfall-risk classification.

LAST UPDATED โ€”
ACTIVE ZONE Meerut
โ—’
Forecast Rainfall โ€” 48-Hour Forecast
!
Inundation Risk โ€” current model output
โ‰ˆ
Predicted Inundation โ€” estimated area
โ—ท
Warning Lead Time โ€” before critical threshold
SPATIAL INTELLIGENCE

Live Risk Map

Low Moderate High Critical
LIVE AI / ML SIGNAL

Rainfall Risk Intelligence

ML ACTIVE
Significant rainfall probability โ€”
Current classification โ€”
Model Random Forest Classifier
Warning threshold โ‰ฅ 5 mm / 6h
TOP PREDICTIVE FEATURES
24h rainfall accumulation 22.5%
12h rainfall accumulation 13.1%
Cloud cover 9.6%
6h rainfall accumulation 8.5%
Feature importance from the trained Random Forest model. Higher importance indicates greater contribution to model decisions, not causal influence.
MULTI-SOURCE

Data Fusion Status

FUSION ENGINE ACTIVE
๐Ÿ›ฐ Satellite Live IR & VIS Stream
โ—‰ Radar Live Doppler Feed
โ˜ Observations Live AWS Telemetry
โŒ NWP ECMWF & GFS Model
โ–ณ Terrain 30m DEM Mesh Loaded
ACTIONABLE OUTPUT

Priority Alert

โ€”

Loading rainfall conditions...

PREDICTION ENGINE

Rainfall Forecast Intelligence

Time-series forecast view for the selected operational zone.

Rainfall Forecast โ€ข 12 Hour Window

MODEL OUTPUT
FORECAST SUMMARY

Heavy rainfall window

โ€” mm

Expected peak accumulation within the next operational window.

Peak window โ€”
Peak intensity โ€”
Trend โ€”
Risk transition โ€”
FLOOD IMPACT INTELLIGENCE

Predicted Inundation

Translate rainfall forecasts into spatial impact and exposure.

PREDICTED AREA โ€” kmยฒ
EXPOSURE SNAPSHOT
โ–ฃ โ€” road segments
โŒ‚ โ€” critical sites
โ—Œ โ€” priority zones
RISK TRANSITION
Now +2H +4H +6H
โ€” Loading current inundation assessment.
CRITICAL EXPOSURE BREAKDOWN

Vulnerable Infrastructure & Asset Exposure Matrix

Dynamic spatial impact assessment calculated for active zone.

Vulnerable Corridor / Asset Asset Category Water Depth Est. Estimated Exposure Evacuation Priority
DIGITAL TWIN GIS TOPOGRAPHY (DEM 30m)

Topographical Elevation & Flood Inundation Cross-Section Profile

3D SLOPE GRADIENT: 3.4ยฐ

Topological cross-section analysis illustrating water accumulation in low-lying terrain depressions for active zone.

MIN ELEVATION (RIVER BED): 42m AGL
URBAN BASIN: 48m AGL
SUBSTATION: 56m AGL
NDRF RELIEF HIGH-GROUND: 78m AGL
EARLY WARNING

Alert Center

Location-specific warnings designed for faster response.

โ€” LIVE

Loading location...

Loading current rainfall conditions...

Rainfall โ€”
Lead time โ€”
Confidence โ€”
RESPONSE PLAYBOOK

Recommended Actions

01 Increase monitoring of low-lying zones.
02 Review road and drainage vulnerability.
03 Prepare local response teams.
04 Issue targeted public advisories if thresholds are crossed.
ACTIVE OPERATIONAL ZONE

Active Zone

โ€”
Latitude โ€”
Longitude โ€”
Inundation โ€”
Updated โ€”
AI SAFE EVACUATION & RELIEF PATHFINDER

Real-Time Evacuation Corridors & Shelter Capacity

OPTIMIZED ROUTING ACTIVE
DESIGNATED NDRF RELIEF CAMPS (NEARBY)
DATA ENGINEERING

Multi-Source Data Fusion

One operational picture built from complementary weather and geospatial sources.

๐Ÿ›ฐ Satellite Cloud / rainfall context
โ†’
โ—‰ Radar Precipitation structure
โ†’
โ˜ Observations Ground truth
โ†’
โŒ NWP Forecast guidance
โ†’
โ–ณ Terrain Spatial vulnerability
FUSION LAYER

Spatio-temporal feature alignment

Inputs are standardized into a common operational grid before prediction and risk scoring.

Spatial alignment 100%
Temporal synchronization 96%
Data availability 100%
AI / ML

Model Intelligence

Transparent, measurable and explainable machine-learning components powering rainfall early-warning intelligence.

01

Rainfall Prediction

Machine-learning based rainfall estimation using historical meteorological features and temporal rainfall signals.

Model Random Forest
Forecast horizon 48 hours
02

Rainfall Event Classifier

Classifies whether significant rainfall is likely to occur within the operational warning window.

Threshold โ‰ฅ 5 mm / 6h
Output Probability + Risk
03

Risk Classification

Converts rainfall signals and downstream indicators into operational severity categories for early warning.

Risk levels 4 classes
Explainability Enabled
VALIDATED BASELINE

Rainfall Event Classifier

TEST SET
ROC-AUC 0.904
PR-AUC 0.279
Precision 25%
Recall 42%
F1 Score 31%
Test set: 10,516 hourly samples. Positive class: โ‰ฅ 5 mm accumulated rainfall over the next 48 hours. The current prototype demonstrates probabilistic early-warning inference. Threshold calibration and additional data sources remain part of the operationalization roadmap.

ROC-AUC Curve (0.904 AUC)

Feature Importance Ranking

REGRESSION BASELINE

6-Hour Rainfall Estimation

RANDOM FOREST
MAE 0.692 mm
RMSE 2.122 mm
Rยฒ 0.094
Baseline regression performance on the held-out test set. The zero-inflated rainfall distribution motivates the complementary event-based classification approach.
LIVE INFERENCE

Current Model Output

ML ACTIVE
Significant rainfall probability โ€”
Risk classification โ€”
Model Random Forest
Warning threshold โ‰ฅ 5 mm / 6h
SYSTEM DESIGN

End-to-End Architecture

From raw observations to actionable disaster intelligence.

DATA LAYER
Satellite
Radar
Weather Stations
NWP
Terrain / GIS
โ†“
PROCESSING
Cleaning
Spatial Alignment
Feature Engineering
Data Fusion
โ†“
AI / ML
Rainfall Model
Inundation Model
Risk Engine
Explainability
โ†“
APPLICATION
GIS Dashboard
Alert Engine
Scenario Simulator
Decision Support
REST API
โ†“
USERS
Disaster Authorities
Emergency Teams
Local Administration
OPERATIONAL SCENARIO ENGINE

Precipitation Stress-Test Simulator

Adjust meteorological parameters to execute predictive stress-test simulations across local drainage models.