Deep Neural Networks for Air Pollution Forecasting
Keywords:
Air Pollution, Forecasting, Deep Learning, Prediction, Hybrid ModelsAbstract
This study explores the use of deep learning models for forecasting air pollution, specifically PM2.5 levels, using data from the Central Pollution Control Board (CPCB). The methodology involves extensive data preprocessing, including trend identification, missing value handling, and the extraction of temporal features to capture seasonal variations. The models evaluated include standalone LSTM, Hybrid LSTM-1D CNN, Hybrid LSTM-GRU, and Hybrid GRU-1D CNN. An 80:20 training-testing split was used, with feature extraction methods applied to enhance predictive accuracy. The performance of the models was assessed using common metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2). The Hybrid GRU-1D CNN model demonstrated the best performance, achieving the lowest MSE (201.2), RMSE (14.1), and MAE (6.7), with a high R2 value of 0.99. These results highlight the potential of hybrid deep learning models for accurate air pollution prediction, offering valuable insights for environmental monitoring and policymaking.
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