Abstract
One of the global environmental challenges is air pollution that has severe implications for human health and the ecosystem. The general factors affected in the air quality are emissions from diverse sources, meterological conditions, and regional topography. The air pollutant measures how much amount of materials present in the air and the mainly affected particulate materials are PM10 and PM2.5. PM10 represents small particles in dust and smoke, but PM2.5 is a fine particle matter that is more dangerous. For acquiring efficient public health interventions and environmental management, the accurate air quality prediction models are required. This research paper proposes a transformer-based deep learning model for multi-region air quality prediction. The transformer model’s self-attention mechanism enables it to handle complex spatiotemporal dependencies in air quality data from multiple regions simultaneously. This proposed model develops a robust and efficient air quality prediction model and integrates the crossover boosted dynamic arithmetic optimization (CDAO) algorithm for hyperparameter tuning. The proposed transformer-based model is optimized to forecast future pollutant concentrations in different regions based on historical air quality data, meteorological parameters, and geographical information. CDAO dynamically explores the hyperparameter space, efficiently finding the optimal set of hyperparameters for the transformer model, leading to better results achievement. The research employs air quality data in India for the study period from 2018 to 2022. Data preprocessing includes handling missing values and normalization, ensuring data consistency. The model achieves better outcomes in accurately predicting air pollutant concentrations across multiple regions compared to baseline models and other hyperparameter tuning techniques. The CDAO algorithm significantly reduces the convergence time and enhances the model’s performance, leading to better results attainment.
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Panneerselvam, V., Thiagarajan, R. Toward accurate multi-region air quality prediction: integrating transformer-based deep learning and crossover boosted dynamic arithmetic optimization (CDAO). SIViP (2024). https://doi.org/10.1007/s11760-024-03061-z
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DOI: https://doi.org/10.1007/s11760-024-03061-z