Air Pollutants Classification Using Optimized Neural Network Based on War Strategy Optimization Algorithm

Air quality prediction is considered one of complex problems. This is due to volatility, dynamic nature, and high variability in space and time of particulates and pollutants. Meanwhile, designing an automated model for monitoring and predicting air quality becomes more and more relevant, particular...

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Bibliographic Details
Published in:Automatic control and computer sciences 2023-12, Vol.57 (6), p.600-607
Main Authors: Gehad Ismail Sayed, Aboul Ella Hassanein
Format: Article
Language:English
Subjects:
Air monitoring
Air pollution
Air quality
Algorithms
Classification
Computer Science
Control Structures and Microprogramming
Machine learning
Neural networks
Optimization
Particulates
Pollutants
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Summary:Air quality prediction is considered one of complex problems. This is due to volatility, dynamic nature, and high variability in space and time of particulates and pollutants. Meanwhile, designing an automated model for monitoring and predicting air quality becomes more and more relevant, particularly in urban regions. Air pollution can significantly affect the environment and eventually citizens’ health. In this paper, one of the popular machine learning algorithms, the neural network algorithm, is employed to classify different species of air pollutants. To boost the performance of the traditional neural network, the war strategy optimization algorithm tunes the neural network’s parameters. The experimental results demonstrate that the proposed optimized neural network based on the war strategy algorithm can accurately classify air pollutant species.
ISSN:0146-4116
1558-108X
DOI:10.3103/S0146411623060081