Air Pollution Forecasting using Hybrid Deep Learning Models: A Comparative Study of ConvLSTM and LSTM-GRU Architectures
Keywords:
Air pollution, forecasting, deep learning, 1D ConvNets, Bidirectional GRUAbstract
For environmental decision-making and public health protection, particularly in rapidly developing regions, reliable estimates of air pollution levels are crucial. With a focus on PM2.5 concentrations in North Central India, this study presents a deep learning-based system that effectively predicts air pollutant levels. The CPCB, or Central Pollution Control Board, provided the data, which includes 18,776 entries covering nine main pollutants. Much preparation, including filling in those that were missing, normalising the data and constructing time-series features, went into splitting the data into training (80%) and testing (20%) groups. We employed a ConvLSTM and a Hybrid LSTM-GRU, two state-of-the-art deep learning models, to grasp the intricate temporal relationships in the data. The results of the evaluation reveal that compared to the ConvLSTM model, the Dual LSTM-GRU model performs better when it comes to prediction. The ConvLSTM model's MSEs were 0.254 and 0.276, respectively, while this one had training and validation MSEs of 0.187 and 0.203. R², RMSE and MAE are only a few of the metrics that demonstrate the hybrid model's superior performance. Based on these findings, hybrid deep learning architectures may be useful in developing accurate, real-time air quality forecasting systems, which in turn can aid in pollution management via the facilitation of prompt responses and well-informed decisions.
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