COD content estimation of multi-feature fusion based on water transmitted spectrum

The chemical oxygen demand (COD) is a parameter that can quickly detect the organic pollutants and can well reflect the degree of water pollution. A COD content estimation model of multi-feature fusion water based on transmitted spectrum measurement was proposed. The transmission hyperspectral method collected 100 groups of COD water spectral information, preprocessed the spectral data, selected the characteristic band, analyzed the influence of different preprocessing methods on the model accuracy, carried out the feature fusion, and established the BP neural network model. By comparing the accuracy of the model, the optimal model was selected to detect the water COD content. The results show that the decision coefficient R² of BP neural network model based on multi-feature fusion is 0.991 64, and the root-mean-square error (RMSE) is 0.030 9. Compared with the partial least square method, the model has higher goodness of fit and higher accuracy.The multi-feature fusion based BP neural network hyperspectral detection method can realize the detection of COD content in water, and can be applied to the detection of other components in water.