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<p>The Najaf Sea is increasingly affected by seasonal tidal pollution, raising significant concerns for both environmental integrity and public health. In this study, we explore the hidden optical fingerprints of its water under varying pollution levels. Using a HIGHTOP visible spectrophotometer, we recorded absorbance spectra from 220 to 1020&#xa0;nm for both polluted and clean samples. The spectral behavior revealed complex, nonlinear patterns. Artificial neural networks (ANNs) were employed to predict these patterns. The results showed distinct spectral differences and a high correlation (<i>R</i> &gt; 0.95) between predicted and actual values, confirming the ANN’s effectiveness in modeling water absorbance and supporting its application in environmental monitoring.</p>

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Smart spectral analysis of Najaf Sea water using artificial neural networks

  • 期刊论文

  • 发表日期：

    2026年5月23日

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  • Hassanin Mohammed Hamza,
  • S. Mostafa Safavihamami,
  • Yaser Norouzi

摘要

The Najaf Sea is increasingly affected by seasonal tidal pollution, raising significant concerns for both environmental integrity and public health. In this study, we explore the hidden optical fingerprints of its water under varying pollution levels. Using a HIGHTOP visible spectrophotometer, we recorded absorbance spectra from 220 to 1020 nm for both polluted and clean samples. The spectral behavior revealed complex, nonlinear patterns. Artificial neural networks (ANNs) were employed to predict these patterns. The results showed distinct spectral differences and a high correlation (R > 0.95) between predicted and actual values, confirming the ANN’s effectiveness in modeling water absorbance and supporting its application in environmental monitoring.