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Volume 3, Issue 1, 2025
Open Access
Research article
Benzene Pollution Forecasting by Recurrent Neural Networks Tuned with Adapted Elk Heard Optimizer
dejan bulaja ,
tamara zivkovic ,
milos pavkovic ,
vico zeljkovic ,
nikola jovic ,
branislav radomirovic ,
miodrag zivkovic ,
nebojsa bacanin
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Available online: 03-30-2025

Abstract

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Benzene is a toxic airborne contaminant and a recognized cancer-causing agent that presents substantial health hazards even at minimal concentrations. The precise prediction of benzene concentrations is crucial for reducing exposure, guiding public health strategies, and ensuring adherence to environmental regulations. Because of benzene's high volatility and prevalence in metropolitan and industrial areas, its atmospheric levels can vary swiftly influenced by factors like vehicular exhaust, weather patterns, and manufacturing processes. Predictive models, especially those driven by machine learning algorithms and real-time data streams, serve as effective instruments for estimating benzene concentrations with notable precision. This research emphasizes the use of recurrent neural networks (RNNs) for this objective, acknowledging that careful selection and calibration of model hyperparameters are critical for optimal performance. Accordingly, this paper introduces a customized version of the elk herd optimization algorithm, employed to fine-tune RNNs and improve their overall efficiency. The proposed system was tested using real-world air quality datasets and demonstrated promising results for predicting benzene levels in the atmosphere.
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