Nation-Scale Air Quality Prediction using Time-Series Machine Learning

The Problem

Air pollution alerts are currently issued well after conditions have already become hazardous to human health. Authorities fundamentally need robust prediction capabilities, not just passive monitoring, to implement preventative measures.

Data Pipeline & Approach

Building an accurate forecasting model required handling complex, noisy, real-world data from varied sources:

• Data Ingestion & Cleaning: Aggregated vast datasets directly from the Central Pollution Control Board (CPCB) and OpenAQ for major metropolitan areas including Delhi-NCR, Bangalore, Mumbai, and Varanasi.
• Target Metrics: Designed the pipeline to specifically target and predict critical air quality markers including aggregate AQI, PM2.5, and PM10 particulate matter.
• Model Architecture Comparison: Rather than relying on a single algorithm, I built and scientifically compared three distinct forecasting paradigms:
  
  1. LSTM Neural Networks: A deep learning approach capable of understanding long-term non-linear dependencies.
  2. Facebook Prophet: A robust additive regression model highly effective with daily and yearly seasonality.
  3. ARIMA: A classic statistical baseline for autoregressive integrated moving averages.

Evaluation & Results

All models were rigorously evaluated using standard regression metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the R² coefficient of determination.

The study overwhelmingly proved that the deep learning (LSTM) approach successfully captured complex, non-linear seasonal patterns and extreme pollution spikes far more effectively than classical statistical models, offering a viable blueprint for preemptive governmental air quality management.