Article: Advanced air quality prediction modelling using intelligent optimisation algorithm in urban regions Journal: International Journal of Sensor Networks (IJSNET) 2025 Vol.49 No.2 pp.123 - 134 Abstract: Ambient air contamination is a significant environmental challenge threatening human well-being and quality of life, especially in urban areas. Technical breakthroughs in artificial intelligence models offer more accurate air quality predictions by analysing significant data sources, including meteorological factors like humidity, wind speed, and pollution data. Also, existing methods of air quality prediction often lack due to their dependency on statistical models that may not adequately capture the complexities of environmental data like pollutants and meteorological factors. Thus, the research introduces a grey wolf optimised variational autoencoder to enhance the air quality prediction by effectively capturing complex relationships in environmental data. The model acquires the probabilistic nature of variational latent representations from historical air quality input data and prevents overfitting. The relevant features are selected using the grey wolf technique, identifying the appropriate variables to enhance the data quality. Additionally, it optimises critical hyperparameters like learning rates and greedy layer sizes, leading to better convergence during model training and improved performance in air quality index prediction. Experimental results demonstrate improved prediction accuracy, reduced error rate, and faster convergence. Inderscience Publishers - linking academia, business and industry through research

Title: Advanced air quality prediction modelling using intelligent optimisation algorithm in urban regions

Authors: Wendi Tan; Zhisheng Li

Addresses: School of Liberal Education, Liuzhou Railway Vocational Technical College, GuangXi, China ' School of Environment, Liaoning University, ShenYang Liaoning, China

Abstract: Ambient air contamination is a significant environmental challenge threatening human well-being and quality of life, especially in urban areas. Technical breakthroughs in artificial intelligence models offer more accurate air quality predictions by analysing significant data sources, including meteorological factors like humidity, wind speed, and pollution data. Also, existing methods of air quality prediction often lack due to their dependency on statistical models that may not adequately capture the complexities of environmental data like pollutants and meteorological factors. Thus, the research introduces a grey wolf optimised variational autoencoder to enhance the air quality prediction by effectively capturing complex relationships in environmental data. The model acquires the probabilistic nature of variational latent representations from historical air quality input data and prevents overfitting. The relevant features are selected using the grey wolf technique, identifying the appropriate variables to enhance the data quality. Additionally, it optimises critical hyperparameters like learning rates and greedy layer sizes, leading to better convergence during model training and improved performance in air quality index prediction. Experimental results demonstrate improved prediction accuracy, reduced error rate, and faster convergence.

Keywords: air quality index; AQI; prediction model; optimisation; artificial intelligence; urban region; environmental factors.

DOI: 10.1504/IJSNET.2025.149114

International Journal of Sensor Networks, 2025 Vol.49 No.2, pp.123 - 134

Received: 18 Feb 2025
Accepted: 24 Feb 2025
Published online: 14 Oct 2025 *

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Title: Advanced air quality prediction modelling using intelligent optimisation algorithm in urban regions

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