india-air-quality-predictor

🇮🇳 India Air Quality Prediction System

AI-powered Artificial Neural Network for forecasting air pollution in Indian tier-2 cities.

This project implements a Bidirectional LSTM with Attention neural network to predict PM2.5, PM10, NO2, SO2, O3, CO, and AQI for the next 6 hours based on 24 hours of historical data.

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🎯 Target Cities

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🧠 Model Architecture

Input (24h × 93 features)
    ↓
Bidirectional LSTM (64 hidden, 1 layer)
    ↓
Attention Mechanism
    ↓
MLP Decoder (256 → 128 → 42)
    ↓
Output (6h × 7 targets: PM2.5, PM10, NO2, SO2, O3, CO, AQI)

Parameters: ~161K
Framework: PyTorch

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📊 Performance Metrics

Metrics computed on held-out test set with inverse-transformed (original scale) values.

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🗂️ Dataset

The dataset was generated using realistic pollution profiles based on CPCB (Central Pollution Control Board) documented patterns for Indian cities:

• Period: 3 years of hourly data (2023-2025)
• Records: 157,680 per city
• Features: Pollutants, meteorology, temporal cyclical encodings, lag features, rolling statistics
• Source: Synthetic, parameterized from published Indian air quality literature

Dataset Structure

data/india_air_quality_synthetic.csv
├── datetime, city
├── pm25, pm10, no2, so2, o3, co
├── temperature, humidity, wind_speed
├── hour, day_of_week, month, day_of_year
├── aqi, aqi_category

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🚀 Usage

Training the Model

pip install -r requirements.txt
python generate_synthetic_data.py  # Create dataset
python preprocess.py               # Feature engineering
python train.py                    # Train ANN

Running the Prediction App

python app.py

Python API

import torch
from model import get_model

model = get_model(model_type='lstm_attn', input_dim=93)
model.load_state_dict(torch.load('models/best_model.pt')['model_state_dict'])
# Input: (batch, 24h, 93_features) -> Output: (batch, 6h, 7_targets)

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📁 Repository Structure

├── data/                          # Raw and processed data
├── processed/                     # Scaled sequences for training
├── models/                        # Trained checkpoints
├── model.py                       # ANN architectures
├── train.py                       # Training script
├── preprocess.py                  # Data preprocessing pipeline
├── generate_synthetic_data.py     # Dataset generator
├── app.py                         # Gradio prediction interface
└── requirements.txt               # Dependencies

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🔗 References

• CPCB Air Quality Portal: https://airquality.cpcb.gov.in
• CPCB AQI Calculation Methodology (CPCB, 2014)
• AirPhyNet: Physics-Guided Neural Networks for Air Quality Prediction (Hettige et al., 2024)
• AirCast: Multi-Variable Air Pollution Forecasting (Nedungadi et al., 2025)

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⚖️ Disclaimer

The current dataset is synthetic and parameterized from published pollution profiles for demonstration and model development. For production deployment, integrate with real-time CPCB CAAQMS data feeds or OpenAQ API with proper API keys.

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📜 License

Apache-2.0