README.md

metadata

title: PM2.5 Pollution Forecast
emoji: 🌬️
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 4.44.0
python_version: '3.12'
app_file: app.py
pinned: false
license: mit
short_description: PM2.5 forecast demo with saved predictions
datasets:
  - sumit1703/pm25-forecasting-data
tags:
  - gradio
  - air-quality
  - pm25
  - pollution-forecasting
  - deep-learning
  - data-visualization
suggested_hardware: cpu-basic

🌬️ PM2.5 Pollution Forecasting Demo

ANRF AISEHack Phase 2 — Theme 2 — Pollution Forecasting (IIT Delhi)

This demo visualizes predictions from a ConvLSTM + Fourier Neural Operator (FNO) hybrid model trained to forecast PM2.5 air pollution levels across a 140×124 spatial grid over Northern India.

Live Links

• Live Demo: https://huggingface.co/spaces/sumit1703/pm25-forecasting
• Dataset: https://huggingface.co/datasets/sumit1703/pm25-forecasting-data
• GitHub: https://github.com/sumitjadhav1703/pm25-forecasting-demo

Important Note

This Space visualizes precomputed PM2.5 predictions saved from the Kaggle GPU run. It does not run live model inference, training, or torch at runtime.

How to Use

1. Use the Test Window slider to select a time period from the test dataset
2. Use the Forecast Hour slider to select how far ahead (+1h to +16h)
3. Compare the last known PM2.5 map (left) with the model's forecast (right)
4. Read the statistics below the maps

Model Architecture

Component	Details
Encoder	Stacked ConvLSTM (2 layers)
Spatial	Fourier Neural Operator (FNO)
Decoder	UNet with SE blocks
Input	10 hours × 20 atmospheric features × 140×124 grid
Output	16-hour PM2.5 forecast
Training	Kaggle T4 GPU, ~8 hours

Competition Results

• Competition: ANRF AISEHack Phase 2 — Theme 2 (IIT Delhi)
• Team: Binary Bombers
• Phase 2 Rank: 2
• Final Score: 0.8795 (sMAPE-based)

Kaggle Leaderboard Proof

The final private leaderboard for ANRF - AISEHack - Phase 2 - Theme 2 - Pollution Forecasting (IITD) shows:

• Team: Binary Bombers
• Final Rank: 2
• Final Score: 0.8795
• Entries: 21

Note: Kaggle competition pages may require login to view the leaderboard.

Competition link: https://www.kaggle.com/competitions/anrf-aise-hack-phase-2-theme-2-pollution-forecasting-iitd

Dataset

The competition dataset contains 4 months of WRF-simulated atmospheric data: APRIL_16, JULY_16, OCT_16, DEC_16. Features include PM2.5, wind components, temperature, PBLH, and various emission tracers.

Built by Sumit — B.Tech AI & Data Science, JNEC MGM University