A hybrid model for predicting spatial distribution of soil organic matter in a bamboo forest based on general regression neural network and interative algorithm

A general regression neural network model, combined with an interative algorithm (GRNNI) using sparsely distributed samples and auxiliary environmental variables was proposed to predict both spatial distribution and variability of soil organic matter (SOM) in a bamboo forest. The auxiliary environmental variables were: elevation, slope, mean annual temperature, mean annual precipitation, and normalized difference vegetation index. The prediction accuracy of this model was assessed via three accuracy indices, mean error (ME), mean absolute error (MAE), and root mean squared error (RMSE) for validation in sampling sites. Both the prediction accuracy and reliability of this model were compared to those of regression kriging (RK) and ordinary kriging (OK). The results show that the prediction accuracy of the GRNNI model was higher than that of both RK and OK. The three accuracy indices (ME, MAE, and RMSE) of the GRNNI model were lower than those of RK and OK. Relative improvements of RMSE of the GRNNI model compared with RK and OK were 13.6% and 17.5%, respectively. In addition, a more realistic spatial pattern of SOM was produced by the model because the GRNNI model was more suitable than multiple linear regression to capture the nonlinear relationship between SOM and the auxiliary environmental variables. Therefore, the GRNNI model can improve both prediction accuracy and reliability for determining spatial distribution and variability of SOM.