Application of RBF neural network in determining soil heavy metal spatial variability

The Radial Basis Function Neural Network (RBFNN) was used to predict the spatial variability of Cr, Cd and Hg in the top soils of Jinyuan District, Taiyuan City. The RBFNN and Ordinary Kriging interpolation methods were compared for a more appropriate method of predicting the spatial variability of soil heavy metals. The results showed that proper spread parameter of RBFNN was critical for limiting errors and improving overall model accuracy. The optimal spread parameter values for Cr, Cd and Hg were 0.08, 0.10 and 0.14, respectively. These values were usable as the basis of reference for determining the spatial distribution of heavy metals in local farmland soils in the study area. Both the RBFNN and Ordinary Kriging interpolation methods predicted the spatial distribution of soil heavy metals with similar tendencies. Although soil Cd concentration was higher in the central region of research area (and especially in the axis from northeast to southwest), it gradually decreased from the axis to the side regions. Soil Cr concentration was also higher in the central region than in other areas. Soil Hg concentration was higher in the northeast of research area, but also gradually decreased from northeast to southwest. Generally, the spatial distributions of Cr, Cd and Hg corresponded with the sources of pollution distribution in the research area. For limited sample sizes, the RBFNN method was more sensitive and suitable for predicting the spatial distribution of heavy metals than the Ordinary Kriging method.