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| Simulation Estimation of Nitrite Content in Transmitted Water based on Artificial Neural Network |
| Received:October 09, 2023 Revised:June 12, 2024 |
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| DOI：10.3969/j.issn.2095-1035.2024.07.001 |
| KeyWord:Hyperspectra; Artificial neural networks; Nitrite;Data preprocessing;Estimation mode |
| Author | Institution |
| WANG Cailing |
西安石油大学计算机学院 |
| ZHANG Guohao |
西安石油大学计算机学院 |
| YAN Jingjing |
西安石油大学计算机学院 |
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| Abstract: |
| Nitrite is an important test index of water and has great significance for the evaluation of water quality. In this paper, transmission hyperspectral combined with artificial neural network is used to establish a water nitrite content estimation model. In this experiment, 10 concentrations of nitrogen nitrite standard solution (0.02, 0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.18 and 0.20mg/L are prepared by reagents, and the transmission spectrum of each concentration of nitrite solution in the range of 181.1~1023.1nm was scanned 10 times by OCEAN-HDX-XR microfiber spectrometer, and the average value was taken as the original transmission spectrum of each concentration of nitrite solution. Four spectral preprocessing methods of maximum and minimum uniformization (MMN), standard normal change (SNV), multivariate scattering correction (MSC), and second-order differential (SOD) were used respectively, and the water nitrite content estimation model was established by combining the ANN method, and the optimal model was selected to estimate the nitrite content of water by comparing the accuracy of the model.The results show that the RMSE is 0.032367, the MAE is 0.016895 and the R2 is 0.987403 in the BP-ANN neural network prediction model based on second-order differential preprocessing, which has better fitting effect and higher accuracy than the quadratic rational Gaussian process regression (QR-GPR) and quadratic support vector machine (Q-SVM) prediction model. Based on the above experimental results, an inversion method combining ANN hyperspectral nitrite parameters is proposed, which provides a new method for the dynamic detection of nitrite parameters in water quality. |
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