Spatial prediction of soil organic carbon in coal mining subsidence areas based on RBF neural network

Abstract

AbstractA quantitative research on the effect of coal mining on the soil organic carbon (SOC) pool at regional scale is beneficial to the scientific management of SOC pools in coal mining areas and the realization of coal low-carbon mining. Moreover, the spatial prediction model of SOC content suitable for coal mining subsidence area is a scientific problem that must be solved. Taking the Changhe River Basin of Jincheng City, Shanxi Province, China, as the study area, this paper proposed a radial basis function neural network model combined with the ordinary kriging method. The model includes topography and vegetation factors, which have large influence on soil properties in mining areas, as input parameters to predict the spatial distribution of SOC in the 0–20 and 2040 cm soil layers of the study area. And comparing the prediction effect with the direct kriging method, the results show that the mean error, the mean absolute error and the root mean square error between the predicted and measured values of SOC content predicted by the radial basis function neural network are lower than those obtained by the direct kriging method. Based on the fitting effect of the predicted and measured values, the R2 obtained by the radial basis artificial neural network are 0.81, 0.70, respectively, higher than the value of 0.44 and 0.36 obtained by the direct kriging method. Therefore, the model combining the artificial neural network and kriging, and considering environmental factors can improve the prediction accuracy of the SOC content in mining areas.