Prediction of nickel concentration in peri-urban and urban soils using hybridized empirical bayesian kriging and support vector machine regression

Abstract

Soil pollution is a big issue caused by anthropogenic activities. The spatial distribution of potentially toxic elements (PTEs) varies in most urban and peri-urban areas. As a result, spatially predicting the PTEs content in such soil is difficult. A total number of 115 samples were obtained from Frydek Mistek in the Czech Republic. Calcium (Ca), magnesium (Mg), potassium (K), and nickel (Ni) concentrations were determined using Inductively Coupled Plasma Optical Emission Spectroscopy. The response variable was Ni, while the predictors were Ca, Mg, and K. The correlation matrix between the response variable and the predictors revealed a satisfactory correlation between the elements. The prediction results indicated that support vector machine regression (SVMR) performed well, although its estimated root mean square error (RMSE) (235.974 mg/kg) and mean absolute error (MAE) (166.946 mg/kg) were higher when compared with the other methods applied. The hybridized model of empirical bayesian kriging-multiple linear regression (EBK-MLR) performed poorly, as evidenced by a coefficient of determination value of less than 0.1. The empirical bayesian kriging-support vector machine regression (EBK-SVMR) model was the optimal model, with low RMSE (95.479 mg/kg) and MAE (77.368 mg/kg) values and a high coefficient of determination (R² = 0.637). EBK-SVMR modelling technique output was visualized using a self-organizing map. The clustered neurons of the hybridized model CakMg-EBK-SVMR component plane showed a diverse colour pattern predicting the concentration of Ni in the urban and peri-urban soil. The results proved that combining EBK and SVMR is an effective technique for predicting Ni concentrations in urban and peri-urban soil.

Figure 1

Study area map [The study area maps was created with ArcGIS Desktop (ESRI, Inc, Version 10.7, URL: https://desktop.arcgis.com).]

Figure 2

Flowchart of the study.

Figure 3

Correlation matrix of the elements showing the relationship between predictors and response (Note: The plot includes scatter plots between the element, and the significance levels is based on p < 0,001).

Figure 4

Spatial distribution of the elements [The spatial distribution maps was created with ArcGIS Desktop (ESRI, Inc, Version 10.7, URL: https://desktop.arcgis.com).]

Figure 5

Represent the final predicted map using the hybridized model EBK _SVMR and using Ca_Mg_K as a predictor. [The spatial distribution map was created with RStudio (Version 1.4.1717: https://www.rstudio.com/).]

Figure 6

Component planes for each empirical bayesian kriging -support vector machine (EBK_SVM_SeOM) variable output. [The SeOM maps were created with RStudio (Version 1.4.1717: https://www.rstudio.com/).]

Figure 7

Different clusters classification components [The SeOM map was created with RStudio (Version 1.4.1717: https://www.rstudio.com/).]