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

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Abstract

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.

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DATA AVAILABILITY STATEMENT

In this paper, the dataset is obtained from Air Quality Open Data Platform at aqicn.org/data-platform/covid19/. This dataset contains max, min, standard deviation, and median for each of the air pollutant species (PM2.5, PM10, Ozone …). Additionally, it contains meteorological data (Temperature, Wind, …). It should be mentioned that all air pollutant species are converted to the US EPA standard.

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Authors and Affiliations

  1. School of Computer Science, Canadian International College (CIC), Сairo, Egypt

    Gehad Ismail Sayed

  2. Faculty of Computers and Artificial Intelligence, Cairo University, Сairo, Egypt

    Aboul Ella Hassanein

  3. Scientific Research Group in Egypt (SRGE), Сairo, Egypt

    Gehad Ismail Sayed &  Aboul Ella Hassanein

Authors
  1. Gehad Ismail Sayed
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  2. Aboul Ella Hassanein
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Contributions

For conceptualization, G. Sayed; methodology, G. Sayed.; software, G. Sayed; validation G. Sayed.; formal analysis, G. Sayed; investigation, G. Sayed.; writing—original draft preparation, G. Sayed.; supervision, A. Hassanien. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Gehad Ismail Sayed.

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The authors declare that they have no conflicts of interest.

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Gehad Ismail Sayed, Aboul Ella Hassanein Air Pollutants Classification Using Optimized Neural Network Based on War Strategy Optimization Algorithm. Aut. Control Comp. Sci. 57, 600–607 (2023). https://doi.org/10.3103/S0146411623060081

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  • DOI: https://doi.org/10.3103/S0146411623060081

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