可解释数据驱动的化学测量方法用于预测非光学活跃的水质参数,使用紫外-可见-近红外吸收光谱学和物理化学测量
Yubo Zhao1, Zhou Zhang2, Bingliang Hu3
1Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119 China; University of Chinese Academy of Sciences, Beijing 100049 China.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
|January 29, 2025
概括
这项研究引入了一种新的水质监测方法,使用光谱学和机器学习来预测总,溶解氧和总. 该方法为复杂的水生环境提供了准确和高效的评估.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 频谱学是一种光谱学.
背景情况:
- 对于非光学活性参数 (NAWQPs) 的传统水质监测是劳动密集型的,并且限制了高空间时间分辨率.
- 自动化方法通常需要耗时的化学预处理.
- 光谱学和机器学习的进步为改善监测提供了机会.
研究的目的:
- 开发和验证一种集成的光谱和机器学习方法,用于预测关键的水质参数.
- 评估使用紫外-可见-近红外吸收光谱用于NAWQP监测的可行性.
- 为长江流域的水质评估提供更有效,更准确的方法.
主要方法:
- 紫外线-可见-近红外吸收光谱与物理化学测量的整合.
- 应用 eXtreme渐变增强算法用于预测建模.
- 利用OPTUNA进行超参数优化和SHapley添加式扩展用于模型可解释性.
主要成果:
- 对于总 (纳什-萨克利夫效率:0.944),溶解氧 (0.934) 和总 (0.835) 的高预测精度.
- 实现的低平均绝对百分比错误:TN为7.8%,DO为8.2%,TP为7.7%.
- 证明了紫外线光谱在预测NAWQP中的重要性.
结论:
- 开发的算法为实时水质监测提供了一种新且有效的方法.
- 这种方法克服了传统监测的局限性,使得高时空分辨率评估成为可能.
- 该研究为复杂水生系统的水资源管理提供了宝贵的工具.
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