Inversion of Chemical Oxygen Demand in Surface Water Based on Hyperspectral Data

Chemical oxygen demand (COD) is an important surface water quality evaluation index. The traditional COD detection method needs to use toxic reagents, easily cause secondary pollution and other shortcomings; hyperspectral method can avoid the above shortcomings so that it has a broad application prospect in COD detection. In order to explore the feasibility of indoor inversion of COD concentration of surface water by hyperspectral technology, this paper takes 129 surface water samples in Jilin Province as research objects, divides the sample set into the training set and test set with sample number ratio of about 3 : 1, and uses hyperspectral imaging system to collect DN values of samples and calculate the corresponding spectral reflectance of water bodies. The derivative method is used for data preprocessing. Pearson correlation analysis is used to judge the correlation degree between spectral data and measured COD concentration, and the characteristic spectral data is extracted. A least square support vector machine (PSO-LSSVM) inversion model optimized by particle swarm optimization was established using full and characteristic spectrum data respectively. These models' prediction accuracy and reliability were compared by analyzing the coefficient of determination R2, root mean square error RMSE, and relative percent deviation RPD. The results show that the correlation between COD concentration and spectral reflectance of surface water is significantly enhanced after derivative pretreatment. The prediction results based on derivative spectral data are better than those based on original spectral data. The model based on extracting characteristic spectral data has a better prediction effect than the model based on full spectral data. Among them, the inversion model of surface water's COD concentration established using the first derivative preprocessing method and the characteristic spectrum has the best prediction results. The determination coefficient of verification set R-2=0.856 7, the root mean square error RMSE=3.822 9, and the relative percent deviation RPD=2.641 4. The above research preliminarily confirms the feasibility of indoor inversion of COD concentration in surface water based on hyperspectral data. It provides a new method and idea for applying hyperspectral technology in the detection of COD in surface water.