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WATERLOGGING INTELLIGENCE

Waterlogging Susceptibility โ€” Raipur & Buffer

Grid-based analysis using DEM, Sentinel-1 SAR, WorldCover & TerraClimate via Google Earth Engine.

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Grid Cells
500m
Resolution
6
Data Layers
How Susceptibility Scores Are Calculated

Each 500m grid cell is scored 0โ€“100 using weighted GEE analysis. Higher = more waterlogging prone.

Elevation (25%)

Low-lying areas relative to city โ€” SRTM 30m DEM

Flat Slope (20%)

Flat terrain pools water โ€” DEM-derived slope

Flow Accumulation (20%)

TWI & depression analysis โ€” water collects here

Impervious Surface (15%)

Paved/built-up areas โ€” ESA WorldCover 10m

SAR Wetness (10%)

Sentinel-1 monsoon vs dry โ€” observed flooding

Moisture & Rain (10%)

TerraClimate soil moisture + annual rainfall

Data Sources: USGS SRTM, Copernicus Sentinel-1 SAR, ESA WorldCover v200, IDAHO TerraClimate, JAXA ALOS DEM.
Filters
Susceptibility Legend
70โ€“100 โ€” Very High
50โ€“69 โ€” High
30โ€“49 โ€” Moderate
0โ€“29 โ€” Low
Buffer zone (5 km)
Raipur boundary
Nava Raipur boundary
Amleshwar boundary
Distribution
Very High Risk
โ€”
High Risk
โ€”
Moderate Risk
โ€”
Low Risk
โ€”
Data Note: Analysis uses GEE satellite data. Run python waterlogging_seeder.py to populate grid data.
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