Hybrid Model for Benzene Prediction in Kuwait's Industrial Regions

Hybrid Model for Benzene Prediction in Kuwait's Industrial Regions

Eiman Tamah Al-Shammari (College of Life Sciences, Kuwait University, Kuwait)
Copyright: © 2024 |Pages: 23
DOI: 10.4018/IJAGR.362003
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Abstract

This study introduces a novel hybrid model to enhance the prediction of benzene concentrations in three industrial regions in Kuwait, utilizing air quality data from 2022 to 2024. The hybrid model, developed through stacking techniques, integrates multiple ML algorithms to employ their collective strengths. The initial analysis involved examining pollutant trends and correlations among benzene, toluene, ethylbenzene, and xylenes (BTEX) compounds. We applied more than ten individual machine learning models to predict benzene levels. We then applied a hyperparameter, tuning the hybrid model to further enhance its prediction performance. By combining these models, the hybrid approach demonstrated superior predictive performance, evaluated using R-squared and mean squared error metrics. The results underscore the effectiveness of the hybrid model in providing accurate benzene concentration prediction, offering valuable insights for air quality management and pollution control in industrial regions.
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Literature Review

Benzene, a volatile organic compound (VOC), is a significant environmental and public health concern, classified as a Group I carcinogen by the International Agency for Research on Cancer (IARC). Anthropogenic activities, such as vehicular exhaust, petroleum refining, and industrial processes, primarily emit it, with secondary contributions from natural phenomena and indoor sources (Agency for Toxic Substances and Disease Registry, 2007; Notario, Gutiérrez-Álvarez, & Adame, 2020). Benzene concentrations are notably high in industrialized and urbanized regions, particularly where refinery operations dominate, as in Kuwait. These emissions pose a dual challenge: safeguarding public health and mitigating environmental impacts.

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