Water Quality Classification Using Convolution Neural Network Based on UV-Vis Spectroscopy
CHEN Qing1, TANG Bin1, 2*, LONG Zou-rong1, 2, MIAO Jun-feng1, HUANG Zi-heng1, DAI Ruo-chen1, SHI Sheng-hui1, ZHAO Ming-fu1, ZHONG Nian-bing1
1. Chongqing Key Laboratory of Fiber Optic Sensor and Photodetector, Chongqing University for Technology, Chongqing 400054, China
2. Key Laboratory of Optoelectronic Technology and System, Ministry of Education, Chongqing University, Chongqing 400044, China
Abstract：The timely and accurate location of water pollution sources and fine pollution prevention and control measures are the urgent need to win the battle of water pollution prevention and control, in order to solve the practical problem of accurate classification of permanganate index of surface water samples, in this paper, based on spectral noise reduction and spectral effective information extraction, according to the characteristics of UV-visible spectral data, one-dimensional convolution neural network is proposed to process UV-visible spectral data. In order to verify the feasibility of detecting a one-dimensional convolution neural network to classify the spectral signals of surface water, a section of the Yangtze River was selected as the sampling point. The water from the upper reaches of the Yangtze River, a river and the Jialing River were collected on the same day, and domestic sewage and 500 mg·L-1 potassium hydrogen phthalate solution were used to simulate the polluted water source. Several kinds of water samples were used to simulate the basin’s changes in water pollution on the same day according to different proportions. Collect the spectral data of existing single and mixed water samples, and distinguish them according to the characteristic spectral information of all kinds of water samples. Realize the prediction and classification of surface water permanganate index, quickly determine the pollution source of abnormal water samples through simulation experiments, optimize the model parameters and complete the optimization training. Compared with traditional classification methods such as the K nearest neighbor method and support vector machine, this algorithm has great advantages in spectral preprocessing complexity and qualitative analysis accuracy. 350 spectral data obtained are used to establish a water quality classification model, of which 245 data are randomly selected as the training set and 105 data as the test set. The confusion matrix classification accuracy of the model is up to 99.0%. It not only simplifies the whole spectral analysis process but also retains more effective spectral information, reduces the influence of artificial pretreatment on UV-Vis spectral data, and realizes the accurate classification of the permanganate index of surface water. The experimental results show that this method can accurately classify water samples from different water bodies, locate pollution sources quickly, and provide a research basis for tracing the sources of pollutants that can not stimulate fluorescence. It provides the possibility for rapid and accurate location of surface water pollution sources with the aid of three-dimensional fluorescence technology. It shows that depth learning has great application potential and research value in the UV-vis spectroscopy measurement of actual water samples.
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