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 (R2) between predicted value and true values of TOC concentrations is 0.927 with PLS model and 0.953 with CNN model, R2 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 (R2) even with small number of samples and can be used for online water quality monitoring combined with UV-Vis spectrometry in agricultural catchment.
Keywords: Convolutional neural networks; Total organic carbon; Total suspended solids; Turbidity compensation; UV–Vis spectrophotometry.
© 2022. The Author(s).