A support vector regression model to predict nitrate-nitrogen isotopic composition using hydro-chemical variables

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-NO 3 − ) in a ruralurban 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-NO 3 − 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-NO 3 − .

Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China

“…It seeks the optimal plane for solving the original low-dimensional problem by transforming it into a high-dimensional space through kernel functions. Additionally, the goal of SVM is to obtain the optimal solution under the available information rather than the optimal solution under the number of samples and thus is suitable for problems with small samples [59,60]. By changing its kernel function and related parameters, SVM can be divided into two categories, support vector classification (SVC) and support vector regression (SVR), and the radial basis function is chosen as the kernel function of SVR in this paper.…”

“…By changing its kernel function and related parameters, SVM can be divided into two categories, support vector classification (SVC) and support vector regression (SVR), and the radial basis function is chosen as the kernel function of SVR in this paper. Radial basis functions are widely used for different dimensional and sample problems with their powerful nonlinear mapping ability [60]. However, the accuracy of the model simulation is largely affected by the penalty parameter C and the kernel parameters, so it needs to be optimized.…”

Intelligent grading of kaffir lime oil quality using non-linear support vector machine

“…As parameter γ increases, NSVM tends to overfit, which means that all training instances are used as support vectors-assigning a smaller value to γ results in underfitting, causing all instances to be grouped. Therefore, an appropriate value must be chosen for the kernel width [26].…”

Rapid Determination of Nitrate Nitrogen Isotope in Water Using Fourier Transform Infrared Attenuated Total Reflectance Spectroscopy (FTIR-ATR) Coupled with Deconvolution Algorithm

“…Therefore, rapid, accurate, and affordable detection techniques of isotopic composition are extremely important. Usually, an isotope ratio mass spectrometer (IRMS) is a necessary instrument for measuring stable isotopic composition [ 10 ]. However, the IRMS is limited for many organizations and institutions due to its high price and maintenance costs.…”