Prediction of total nitrogen in water based on UV spectroscopy and Bayesian optimized least squares support vector machine

Abstract

The total nitrogen (TN) is a major factor contributing to eutrophication and is a crucial parameter in assessing surface water quality. Accurate and rapid methods are crucial for determining the TN content in water. Herein, a fast, highly sensitive, and pollution-free approach is proposed, which combines ultraviolet (UV) absorption spectroscopy with Bayesian optimized least squares support vector machine (LSSVM) for detecting TN content in water. Water samples collected from sampling points near the Yangtze River basin in Chongqing of China were analyzed using national standard methods to measure TN content as reference values. The prediction of TN content in water was achieved by integrating the UV absorption spectra of water samples with LSSVM. To make the model quickly and accurately select the optimal parameters to improve the accuracy of the prediction model, the Bayesian optimization (BO) algorithm was used to optimize the parameters of the LSSVM. Results show that the prediction model performs well in predicting TN concentration, with a high coefficient of prediction determination (R²=0.941 3) and a low root mean square error of prediction (RMSE=0.077 9 mg/L). Comparative analysis with previous studies indicates that the model used in this paper achieves lower prediction errors and superior predictive performance.

Keywords

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References

[1] Zuo H, Xu J, Wang X J. et al.. Research and application of instruments for online monitoring water quality of total nitrogen. Electronic measurement technology, 2021, 44(14): 173-176[J]

[2] Liu B, Liu J R, Wang Y. et al.. Simultaneous determination of sulfide, cyanide, total phosphorus, and total nitrogen in water by dual-system ion chromatography. Chinese journal of inorganic analytical chemistry, 2023, 13(04): 349-355[J]

[3] Wu W, Lai X C, Liu L. et al.. Evaluation of uncertainty of ammonia nitrogen concentration in water by gas-phase molecular absorption spectrometry. Contemporary chemical industry, 2023, 52(08): 1947-1949[J]

[4] Liu J, Zhang J, Tan Z. et al.. Detecting the content of the bright blue pigment in cream based on deep learning and near-infrared spectroscopy. Spectrochimica acta part A: molecular and biomolecular spectroscopy, 2022, 270: 120757. J]

[5] Liu Y, Xu L, Zeng S. et al.. Rapid detection of mussels contaminated by heavy metals using near-infrared reflectance spectroscopy and a constrained difference extreme learning machine. Spectrochimica acta part A: molecular and biomolecular spectroscopy, 2022, 269: 120776. J]

[6] Mesquita D P, Quintelas C, Amaral A L. et al.. Monitoring biological wastewater treatment processes: recent advances in spectroscopy applications. Reviews in environmental science and bio/technology, 2017, 16(3): 395-424. J]

[7] Liu J M, Zhang Y, Yuan D. et al.. Empirical estimation of total nitrogen and total phosphorus concentration of urban water bodies in China using high-resolution IKONOS multispectral imagery. Water, 2015, 7(11): 6551-6573. J]

[8] Liu C F, Zhang F, Ge X. et al.. Measurement of total nitrogen concentration in surface water using hyperspectral band observation method. Water, 2020, 12(7): 1842. J]

[9] Li A M, Xu Y C, Lan X Y. et al.. Particle swarm algorithm to optimize SVR for remote sensing inversion of total nitrogen in Tende Lake. The administration and technique of environmental monitoring, 2023, 35(06): 22-27[J]

[10] Suykens J A K, De Brabanter J, Lukas L. et al.. Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing, 2002, 48(1–4): 85-105. J]

[11] Liao R, Zheng H, Grzybowski S. et al.. Particle swarm optimization-least squares support vector regression based forecasting model on dissolved gases in oil-filled power transformers. Electric power systems research, 2011, 81(12): 2074-2080. J]

[12] Samui P, Kothari D P. Utilization of a least square support vector machine (LSSVM) for slope stability analysis. Scientia iranica, 2011, 18(1): 53-58. J]

[13] Long B, Xian W, Li M. et al.. Improved diagnostics for the incipient faults in analog circuits using LSSVM based on PSO algorithm with Mahalanobis distance. Neurocomputing, 2014, 133: 237-248. J]

[14] Yu Y N, Qi Q S. A study on optimization of the LSSVM model for crane load spectrum regression prediction. Journal of vibration and shock, 2022, 41(12): 215-228[J]

[15] Cai Y, Hao S, Wen X. et al.. Short-term power prediction by using least square support vector machine with variational mode decomposition in a photovoltaic system. IEEE access, 2023, 11: 143486-143500. J]

[16] Feng R J, Chen Z G, Yi S J. Identification of corn varieties based on Bayesian optimization SVM. Spectroscopy and spectral analysis, 2022, 42(06): 1698-1703[J]

[17] Li Y R, Zhang Y L, Wang J C. Survey on Bayesian optimization methods for hyper-parameter tuning. Computer science, 2022, 49(S1): 86-92. J]

[18] Li D S, Li L N, Zhang R C. Water quality pH value determination for visible-near infrared spectroscopy based on SPA and PSO-LSSVM. Laser and optoelectronics progress, 2023, 60(01): 390-395[J]

[19] Ke Y K, Dong H RHandbook of analytical chemistry, 20153rd ed.BeijingChemical Industry Press8-9[M]

[20] Araújo M C U, Saldanha T C B, Galvao R K H. et al.. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics and intelligent laboratory systems, 2001, 57(2): 65-73. J]

[21] Li G W, Gao X H, Xiao N W. et al.. Estimation of soil organic matter content based on characteristic variable selection and regression methods. Acta optica sinica, 2019, 9(9): 0930002[J]

[22] Alruqi M, Sharma P, Deepanraj B. et al.. Renewable energy approach towards powering the CI engine with ternary blends of algal biodiesel-diesel-diethyl ether: Bayesian optimized Gaussian process regression for modeling-optimization. Fuel, 2023, 334: 126827. J]

[23] Vargas Hernandez R A. Bayesian optimization for calibrating and selecting hybrid-density functional models. The journal of physical chemistry A, 2020, 124(20): 4053-4061. J]

[24] Kulamala V K, Kumar L, Mohapatra D P. Software fault prediction using LSSVM with different kernel functions. Arabian journal for science and engineering, 2021, 46(9): 8655-8664. J]

[25] Li S Y, Li S C, Liu Z. et al.. Roughness prediction model of milling noise-vibration-surface texture multi-dimensional feature fusion for N6 nickel metal. Journal of manufacturing processes, 2022, 79: 166-176. J]

[26] Zhang X, Ge Z. Local parameter optimization of LSSVM for industrial soft sensing with big data and cloud implementation. IEEE transactions on industrial informatics, 2019, 16(5): 2917-2928. J]

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