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一种基于1D CNN和2D Gabor转换的水COD检索方法,用于吸收-光光谱
Meng Xia1,2, Ruifang Yang1, Nanjing Zhao1,3
1Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China.
Micromachines
|June 28, 2023
概括
一种新的方法结合了吸收和光光谱,使用融合神经网络来更快,更准确地检测水中的化学氧气需求 (COD). 与传统方法相比,这种方法可以显著减少错误.
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:
- 化学氧气需求 (COD) 是有机水污染的一个关键指标.
- 准确和快速的COD检测对于有效的环境保护至关重要.
- 现有的吸收频谱方法面临着光有机物检索错误的挑战.
研究的目的:
- 开发一种快速同步的方法,使用联合吸收和光光谱来检索化学氧气需求 (COD).
- 提高COD检测的准确性和减少错误,特别是水中的光有机物.
- 引入先进的融合神经网络算法,用于增强水质分析.
主要方法:
- 开发一个融合神经网络算法,集成一维卷积神经网络 (CNN) 和二维Gabor变换.
- 吸收-光光谱融合方法的应用,以分析水样.
- 使用氨基酸水溶液和实际采样的水光谱数据集验证方法.
主要成果:
- 吸收-光COD检索方法在氨基酸溶液中实现了0.32%的相对根平均平方预测误差 (RRMSEP),比单个吸收光谱方法提高了84%.
- 与单个吸收光谱方法相比,COD检索精度达到了98%,增加了15.3%.
- 在真实水样本上,聚变网络显示出卓越的COD测量精度,与吸收频谱CNN网络相比,RRMSEP从5.09%降低到1.15%.
结论:
- 拟议的吸收-光光谱融合方法显著提高了化学氧气需求 (COD) 在水中的检索的准确性和可靠性.
- 融合神经网络算法有效地克服了单频谱方法的局限性,为环境监测提供了一个有前途的工具.
- 这种先进的技术为评估水体中的有机污染水平提供了更精确,更有效的方法.
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