A support vector regression model to predict nitrate-nitrogen isotopic composition using hydro-chemical variables. (15th July 2021)

• Record Type: Journal Article
• Authors: Yang, Yue; Shang, Xu; Chen, Zheng; Mei, Kun; Wang, Zhenfeng; Dahlgren, Randy A.; Zhang, Minghua; Ji, Xiaoliang
• Is Part Of: Journal of environmental management. Volume 290(2021)
• Publication Date: 2021-07-15
• Subjects: Nitrate pollution -- Nitrate-nitrogen isotopic composition (δ15N–NO3−) -- Prediction -- Principal component analysis (PCA) -- Support vector regression (SVR) -- Machine learning model; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2021.112674
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: Nitrate is a prominent pollutant in surface and groundwater bodies worldwide. Isotopes in nitrate provide a powerful approach for tracing nitrate sources and transformations in waters. Given that analytical techniques for determining isotopic compositions are generally time-consuming, laborious and expensive, alternative methods are warranted to supplement and enhance existing approaches. Hence, we developed a support vector regression (SVR) model and explored its feasibility to predict nitrogen isotopic composition of nitrate ( δ 15 N–NO3 − ) in a rural-urban river system in Southeastern China. A total of 16 easily obtained hydro-chemical variables were measured in the wet season (September 2019) and dry season (January 2020) and used to develop the SVR prediction model. The grading method utilized ~75% (35) of the samples for model building while the remaining 11 samples assessed model performance. Principal component analysis (PCA) extracted 7 principal components for SVR model inputs as PCA reduces superfluous variables. We optimized tuning parameters in the SVR model using a grid search technique coupled with V-fold cross-validation. The optimized SVR model provided accurate δ 15 N–NO3 − predictions with a determination coefficient (R 2 ) of 0.88, Nash-Sutcliffe ( NS ) of 0.87, and mean square error ( MSE ) of 0.53‰ in the testing step, and performed much better than the corresponding multivariate linear regression model (R 2 = 0.60, NS = 0.58 and MSE = 1.76‰) and general regression neural network model (R 2 = 0.66, NS = 0.65 and MSE = 1.45‰). Overall, the SVR model provides a potential indirect method to predict environmental isotope values for water quality management that will complement and enhance the interpretation of direct measurements of δ 15 N–NO3 − . Graphical abstract: Image 1 Highlights: SVR model was developed for δ 15 N–NO3 − prediction. Basic hydro-chemical variables were taken into account as input variables. PCA was used to reduce the superfluous variables. SVR model had outstanding efficacy for predicting δ 15 N–NO3 − values. Abstract : An economical and efficient alternative (indirect) method was developed for predicting δ 15 N–NO3 − values based on easily measured hydro-chemical variables.