Scholarlink

<p>Oil spill detection in ice-covered marine environments poses considerable challenges due to fluorescence signal interference from ice, heterogeneous surface properties, and environmental complexity. To address the lack of high-precision oil classification methods under such conditions, this study introduces a fluorescence-based multi-condition classification framework that integrates laser-induced fluorescence (LIF) spectroscopy with a machine learning model optimized by the Golden Sine Algorithm (Gold-SA). LIF spectra were collected for six oil types under four simulated ice coverage and oil volume scenarios, resulting in 24 distinct classification categories. Fluorescence signals underwent denoising using Savitzky-Golay (SG) filtering to improve signal stability and spectral reliability. The resulting Gold-SA-CatBoost model achieved 99.62% accuracy under laboratory conditions within the dataset and 100% accuracy in single-task oil-type identification, surpassing baseline models by a substantial margin. This work demonstrates the efficacy of integrating LIF with advanced optimization-based machine learning for robust oil spill detection under complex icy conditions. The proposed approach provides a viable fluorescence-based strategy for environmental monitoring in cold and polar marine regions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-Condition Classification of Oil Spill in Ice Areas Based on Laser-Induced Fluorescence

  • 期刊论文

  • 发表日期：

    2025年9月16日

查看全文
  • Chenyu Zhao,
  • Ying Li,
  • Qintuan Xu,
  • Yong Wang,
  • Ming Xie,
  • Xiangxiang Ji

摘要

Oil spill detection in ice-covered marine environments poses considerable challenges due to fluorescence signal interference from ice, heterogeneous surface properties, and environmental complexity. To address the lack of high-precision oil classification methods under such conditions, this study introduces a fluorescence-based multi-condition classification framework that integrates laser-induced fluorescence (LIF) spectroscopy with a machine learning model optimized by the Golden Sine Algorithm (Gold-SA). LIF spectra were collected for six oil types under four simulated ice coverage and oil volume scenarios, resulting in 24 distinct classification categories. Fluorescence signals underwent denoising using Savitzky-Golay (SG) filtering to improve signal stability and spectral reliability. The resulting Gold-SA-CatBoost model achieved 99.62% accuracy under laboratory conditions within the dataset and 100% accuracy in single-task oil-type identification, surpassing baseline models by a substantial margin. This work demonstrates the efficacy of integrating LIF with advanced optimization-based machine learning for robust oil spill detection under complex icy conditions. The proposed approach provides a viable fluorescence-based strategy for environmental monitoring in cold and polar marine regions.