Excessive levels of organic matter in water threaten ecological safety and endanger human health. As the water resource environment is deteriorating, accurate and rapid determination of water quality parameters has become a current research hotspot. In recent years, the ultraviolet spectrometry method has been more widely used in the detection of chemical oxygen demand (COD), which is convenient and without chemical reagents. However, this method tends to use absorbance at 254 nm to measure COD. It has a good detection effect when the composition of pollutants is single, but in real life, the complex composition of pollutants will seriously affect the accuracy of measurement. Therefore, a COD prediction model based on ultraviolet-visible (UV-Vis) spectrometry and the convolutional neural network (CNN) is proposed. Compared with other traditional COD prediction models, this model makes full use of the absorbance of all ultraviolet and visible wavelengths, avoiding the information loss caused by using specific wavelengths. Meanwhile, this model is constructed based on the shallow CNN, using convolutional layers with different step lengths instead of the traditional pooling layers, which reduces computation and enhances the capture of spectral feature peaks. Additionally, with the powerful feature extraction capability of the CNN, this model reduces the reliance on pre-processing methods and improves the utilization of spectral information. Experiments have shown that our model has better fitting results and accuracy than other traditional COD prediction models such as the principal component analysis (PCA), partial least squares regression (PLSR), and backpropagation (BP) neural network. This study provides a better solution for improving the accuracy of UV-Vis water quality COD detection, which is conducive to real-time monitoring of the water quality, providing data support of water pollution and its development trend for the government’s water resource protection policy and promoting biodiversity development.
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