Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China.
Environ Monit Assess. 2022 Aug 3;194(9):630. doi: 10.1007/s10661-022-10118-4.
Water quality monitoring is very important in agricultural catchments. UV-Vis spectrometry is widely used in place of traditional analytical methods because it is cost effective and fast and there is no chemical waste. In recent years, artificial neural networks have been extensively studied and used in various areas. In this study, we plan to simplify water quality monitoring with UV-Vis spectrometry and artificial neural networks. Samples were collected and immediately taken back to a laboratory for analysis. The absorption spectra of the water sample were acquired within a wavelength range from 200 to 800 nm. Convolutional neural network (CNN) and partial least squares (PLS) methods are used to calculate water parameters and obtain accurate results. The experimental results of this study show that both PLS and CNN methods may obtain an accurate result: linear correlation coefficient (R) between predicted value and true values of TOC concentrations is 0.927 with PLS model and 0.953 with CNN model, R between predicted value and true values of TSS concentrations is 0.827 with PLS model and 0.915 with CNN model. CNN method may obtain a better linear correlation coefficient (R) even with small number of samples and can be used for online water quality monitoring combined with UV-Vis spectrometry in agricultural catchment.
水质监测在农业集水区非常重要。由于具有成本效益高、速度快且没有化学废物等优点，紫外-可见光谱法已广泛替代传统分析方法。近年来，人工神经网络已在各个领域得到广泛研究和应用。在这项研究中，我们计划使用紫外-可见光谱法和人工神经网络来简化水质监测。采集样本后，立即带回实验室进行分析。在 200 至 800nm 的波长范围内获取水样的吸收光谱。使用卷积神经网络（CNN）和偏最小二乘法（PLS）方法来计算水参数并获得准确的结果。本研究的实验结果表明，PLS 和 CNN 方法都可能获得准确的结果：TOC 浓度的预测值与真实值之间的线性相关系数（R）分别为 0.927（PLS 模型）和 0.953（CNN 模型），TSS 浓度的预测值与真实值之间的线性相关系数（R）分别为 0.827（PLS 模型）和 0.915（CNN 模型）。即使样本数量较少，CNN 方法也可能获得更好的线性相关系数（R），并且可以与农业集水区中的紫外-可见光谱法结合用于在线水质监测。
