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An Air Quality Forecasting Using the Artificial Neural Network in India
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Author(s): T. Jarin (Jyothi Engineering College, India), Shine P. Xavier (A.P.J. Abdul Kalam Technological University, India), R. Muniraj (P.S.R. Engineering College, India), P. Sreeja (A.P.J. Abdul Kalam Technological University, India), Shuai Li (University of Oulu, Finland)and M. Arthi (Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, India)
Copyright: 2025
Pages: 24
Source title:
Expert Artificial Neural Network Applications for Science and Engineering
Source Author(s)/Editor(s): Lingala Syam Sundar (Prince Mohamamd Bin Fahd University, Saudia Arabia), Deepanraj Balakrishnan (Prince Mohammad Bin Fahd University, Saudi Arabia)and Antonio C.M. Sousa (University of Aveiro, Portugal)
DOI: 10.4018/979-8-3693-7250-0.ch010
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Abstract
Accurate and efficient forecasting of the air quality index (AQI) is adequate to improve public health and supporting societal sustainability, despite the challenges posed by air pollution .In this work presents the importance of accurate AQI predictions using the machine learning (ML) approach called Artificial Neural Network (ANN) for improving public health outcomes. Optimized variational mode decomposition (O-VMD) is used for decomposing the AQI samples into various set of samples. The optimal process is demonstrated by the chameleon optimization (CO). The suggested O-VMD improves the ability of the model in capturing complex features. Then, the ANN is used for predicting AQI on the basis of the decomposed elements on two AQI datasets. The suggested O-VMD-ANN demonstrated improved prediction performance compared to other ML model and provided better process for proactive air quality controlling and public health protection in India.
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