✅ Your Direction is Correct. Here’s Why:
The video you shared shows a simplified approach:
- It only uses gas concentrations (e.g., NO₂, SO₂, CO, etc.) as inputs
- It builds a basic ML model (possibly using linear regression or similar)
- It focuses on point-level prediction, not spatial interpolation or advanced mapping
BUT…
🧠 What ISRO Wants (and you’re rightly thinking about):
You’re right — the ISRO hackathon problem wants:
"Advanced estimation of surface-level PM2.5 using satellite AOD, meteorological variables, and ML techniques."
So you're already aligned with their vision of complexity and depth. ✅
🧩 Let’s Clarify Your Components One-by-One
- ✅ Dataset Inputs (Advanced)
You correctly identified:
| Variable | Source | Purpose |
|---|---|---|
| AOD (Aerosol Optical Depth) | INSAT-3DR/3DS | Proxy for pollution seen from space |
| Wind speed/direction | MERRA-2 | Shows how pollution is dispersed |
| Temperature, Humidity, Pressure | MERRA-2 or INSAT | Helps model atmospheric behavior |
| Geo-coordinates (lat, lon) | All sources | Helps place prediction on map |
| Date/Time | Timestamps | Needed for alignment |
| PM2.5 & PM10 | CPCB | Ground truth (label for model) |
✅ Your inclusion of wind, temp, humidity, AOD, and matching them via lat/lon is correct and essential.
- ✅ Model Building
You mentioned:
“We have to train the predictor model from the dataset using technologies like Python’s Scikit-learn, Pandas, Numpy, XGBoost…”
Yes, exactly:
- Pandas/Numpy → data preprocessing
- Scikit-learn → RandomForestRegressor, Linear Regression
- XGBoost → For faster & more accurate tree boosting
- Compare multiple models (RF vs XGBoost vs SVR)
Make sure you:
- Scale data if needed (StandardScaler)
- Use cross-validation
- Evaluate with R², MAE, RMSE
- ✅ Spatial Prediction + Visualization
You rightly said:
“We have to match them with latitude and longitude to show PM2.5 & PM10 values on a map using Leaflet.js probably with OpenStreetMap and show heatmaps.”
Yes, this is precisely the next layer:
- Use lat, lon + model predictions to generate a spatial PM map
- Display in Leaflet.js over OpenStreetMap tiles
- Represent PM2.5/PM10 using:
- CircleMarkers (colored by severity)
- Or heatmap layers (interpolated color gradients)
You can serve your model output as:
- A GeoJSON file with lat, lon, pm25
- A REST API that Leaflet can fetch (Flask/FastAPI backend)
✅ Final Flow Confirmation – You're Spot-On
- INSAT AOD + MERRA-2 + CPCB → Merge by lat/lon + time
- Train ML Model using RF/XGBoost (features: AOD, wind, temp, etc.)
- Predict PM2.5/PM10 across a grid (India-wide)
- Export prediction points with lat/lon as GeoJSON
- Visualize on map with Leaflet + OpenStreetMap
📌 Pro Tips for You
✔️ Do data alignment carefully (time and spatial interpolation)
✔️ Consider inverse distance weighting (IDW) if you want to smooth predictions between points
✔️ If accuracy is low, try adding:
- NDVI (green cover index)
- Elevation (DEM data)
✔️ Test different regressors side-by-side
🧠 What You Can Say to Judges
You can proudly say:
“Unlike simpler solutions, we didn’t just predict PM2.5 using air pollutants. We built a geospatially aware model using satellite AOD and meteorological reanalysis data, trained robust regressors like Random Forest and XGBoost, and visualized results on interactive spatial heatmaps using Leaflet. This allows us to estimate air quality even where ground stations don’t exist — a real value-add for policy and rural health planning.”
🎯 That’s gold in any panel.