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H2MaM: Future Air Quality Prediction using LSTM-based Hyper Heuristic Multi-Chain Model
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  • Sweta Dey ,
  • Kalyan Chatterjee ,
  • Anjan Bandyopadhyay ,
  • Sujata Swain ,
  • Saaketh Mummadi ,
  • Ramagiri Praveen Kumar
Sweta Dey
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Kalyan Chatterjee
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Anjan Bandyopadhyay
Kalinga Institute of Industrial Technology

Corresponding Author:[email protected]

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Sujata Swain
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Saaketh Mummadi
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Ramagiri Praveen Kumar
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Abstract

Air pollution is a critical global concern, demanding precise air quality forecasting to mitigate its severe consequences. Our study introduces a novel Hyper Heuristic Multi-Chain Model (H2MaM) to project future air quality, considering various meteorological factors (MFs) and pollution-related variables like atmospheric pressure, temperature, humidity, and wind patterns. Leveraging 12 units of LSTMs, H2MaM accurately predicts forthcoming air pollutants (APs) concentrations such as PM2.5, CO, and NO2. Additionally, it accounts for spatiotemporal correlations between these APs and MFs, which significantly influence the air quality prediction for the next immediate time interval. H2MaM utilizes a multi-chain mechanism, employing 1-hour prediction models to forecast air quality hourly, enabling approximations for the next 12 hours. Furthermore, it demonstrates the ability to enhance the performance of any predictor. Experimental results substantiate H2MaM’s superiority over various models, including the Support Vector Regressor (SVR), Multi-Layer Perceptron (MLP), Recurrent Air Quality Predictor (RAQP), and Valchogianni models. H2MaM achieves impressive up to 75% better accuracy and consistency compared to SVR, 60% better than MLP, 38% better than RAQP, and 70% better than Valchogianni models.