AI-Driven Air Quality Forecasting Using Multi-Scale Feature Extraction and Recurrent Neural Networks

Journal of Science Technology and Research (JSTAR) 5 (1):575-590 (2024)
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Abstract

We investigate the application of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model for forecasting air pollution levels based on historical data. Our experimental setup uses real-world air quality datasets from multiple regions, containing measurements of pollutants like PM2.5, PM10, CO, NO2, and SO2, alongside meteorological data such as temperature, humidity, and wind speed. The models are trained, validated, and tested using a split dataset, and their accuracy is evaluated using performance metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared.

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