Global Flood Risk and Public Health Impact Assessment

// 1. Define Geographic ROI (Sylhet Division, Bangladesh)
var roi = ee.Geometry.Polygon([
  [[91.30, 24.30], [92.50, 24.30], [92.50, 25.20], [91.30, 25.20]]
]);
Map.centerObject(roi, 9);
Map.setOptions('HYBRID');

// 2. Load Sentinel-1 SAR Collection (VV Polarization, IW mode)
var s1 = ee.ImageCollection('COPERNICUS/S1_GRD')
  .filterBounds(roi)
  .filter(ee.Filter.eq('instrumentMode', 'IW'))
  .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
  .select('VV');

// 3. Define Temporal Windows (Pre-Flood vs Post-Flood 2022)
var preFlood = s1.filterDate('2022-04-15', '2022-05-15').mean().clip(roi);
var postFlood = s1.filterDate('2022-06-15', '2022-06-30').mean().clip(roi);

// 4. Speckle Filtering (3x3 Focal Mean)
var preFiltered = preFlood.focal_mean(30, 'circle', 'meters');
var postFiltered = postFlood.focal_mean(30, 'circle', 'meters');

// 5. Compute Inundation via Change Detection (Ratio/Difference)
var difference = postFiltered.subtract(preFiltered);
var floodThreshold = -3.5;
var rawFlood = difference.lt(floodThreshold);

// 6. Mask Permanent Water & Steep Slopes
var jrcWater = ee.Image('JRC/GSW1_4/GlobalSurfaceWater').select('occurrence');
var permanentWater = jrcWater.gt(80);

var srtm = ee.Image('USGS/SRTMGL1_003');
var slope = ee.Terrain.slope(srtm);
var steepTerrain = slope.gt(5);

var confirmedFlood = rawFlood.where(permanentWater, 0).where(steepTerrain, 0).selfMask();

// 7. Visualization
Map.addLayer(preFiltered, {min: -25, max: 0}, 'Pre-Flood Sentinel-1 (VV)', false);
Map.addLayer(postFiltered, {min: -25, max: 0}, 'Post-Flood Sentinel-1 (VV)', false);
Map.addLayer(confirmedFlood, {palette: ['darkblue']}, 'Extracted Flood Extent');

// 8. Load WorldPop Data (2020 Population Count)
var worldPop = ee.ImageCollection('WorldPop/GP/100m/pop')
  .filterBounds(roi)
  .filter(ee.Filter.eq('year', 2020))
  .mosaic()
  .clip(roi);

// 9. Calculate Exposed Population Density
var exposedPop = worldPop.updateMask(confirmedFlood);

// 10. Spatial Health Risk Proxy (Distance to Flood + Population Density)
// Proximity to standing floodwater increases waterborne risk exposure
var floodDistance = confirmedFlood.fastDistanceTransform(500).sqrt().multiply(30); // in meters
var proximityScore = ee.Image(1).subtract(floodDistance.divide(5000)).clamp(0, 1);

// Normalize Population Density (0-1 scale)
var popMax = 500; // threshold for high density per cell
var popScore = worldPop.divide(popMax).clamp(0, 1);

// Composite Risk Index = Proximity Weight (0.6) + Density Weight (0.4)
var healthRiskIndex = proximityScore.multiply(0.6).add(popScore.multiply(0.4)).updateMask(popScore.gt(0.01));

// 11. Compute Total Exposed Population Metric
var stats = exposedPop.reduceRegion({
  reducer: ee.Reducer.sum(),
  geometry: roi,
  scale: 100,
  maxPixels: 1e9
});

print('Estimated Total Population Exposed to Inundation:', stats.get('population'));

// 12. Visualization of Vulnerability & Risk
Map.addLayer(worldPop, {min: 0, max: 200, palette: ['white', 'yellow', 'orange', 'red']}, 'WorldPop Population', false);
Map.addLayer(exposedPop, {min: 0, max: 100, palette: ['purple', 'magenta']}, 'Exposed Population');
Map.addLayer(healthRiskIndex, {min: 0.2, max: 1.0, palette: ['blue', 'cyan', 'yellow', 'orange', 'red']}, 'Health Risk Exposure Index');

// Export Flooded Area and Health Exposure Risk Map to Google Drive
Export.image.toDrive({
  image: healthRiskIndex.toFloat(),
  description: 'Sylhet_Flood_Health_Risk_2022',
  folder: 'GEE_Health_Disasters',
  fileNamePrefix: 'Sylhet_Health_Risk_Index_2022',
  region: roi,
  scale: 100,
  crs: 'EPSG:4326',
  maxPixels: 1e9
});

// Export Inundation Mask Raster
Export.image.toDrive({
  image: confirmedFlood.toByte(),
  description: 'Sylhet_Flood_Extent_2022',
  folder: 'GEE_Health_Disasters',
  fileNamePrefix: 'Sylhet_Inundation_Mask_2022',
  region: roi,
  scale: 30,
  crs: 'EPSG:4326',
  maxPixels: 1e9
});