Air Quality Index Forecasting with Time-Series Models
Forecast city AQI for the next 72 hours with ARIMA, Prophet and LSTM models, and send health alerts before pollution peaks.
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Problem Statement & Abstract
Air pollution is a serious health issue in Indian cities, and warnings are most useful before the air turns bad. This project forecasts the Air Quality Index days ahead using historical pollution and weather data.
Using public CPCB station data for cities such as Delhi and Ahmedabad, the project cleans gaps, calculates AQI from PM2.5, PM10, NO₂ and other pollutants, and adds weather features (temperature, humidity, wind speed) and calendar features such as festivals like Diwali.
Three forecasting approaches are compared: SARIMA for seasonality, Facebook Prophet for trend and holidays, and an LSTM neural network that learns from multiple variables. Forecasts for the next 72 hours are shown on a Streamlit dashboard with colour-coded AQI categories and health advice, and subscribers receive an alert when "Very Poor" air is expected. The report explains walk-forward validation and why some models suit short-term and others long-term forecasts.
Key Features
- AQI calculation from multiple pollutants
- Weather and festival features
- SARIMA, Prophet and LSTM forecasting compared
- 72-hour forecasts per city
- Walk-forward validation by forecast horizon
- Colour-coded AQI categories with health advice
- Email alerts before “Very Poor” air
- Interactive Streamlit dashboard
Suitable For
Everything included
+ GST • about $96 USD
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- Delivered in 10 to 12 working days
- Free revisions for 30 days while your guide reviews
- Report written to your university’s format