A hybrid model for predicting spatial distribution of soil organic matter in a bamboo forest based on general regression
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ORIGINAL PAPER
A hybrid model for predicting spatial distribution of soil organic matter in a bamboo forest based on general regression neural network and interative algorithm Eryong Liu1 • Jian Liu2 • Kunyong Yu2 • Yunjia Wang3 • Ping He2
Received: 30 September 2018 / Accepted: 26 December 2018 Northeast Forestry University 2019
Abstract 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 Project funding: The article is supported by National Key Research and Development Projects of P. R. China (No.2018YFD0600100). The online version is available at http://www.springerlink.com Corresponding editor: Zhu Hong. & Kunyong Yu [email protected]; [email protected] 1
Fujian Provincial Key Laboratory of Soil Environmental Health and Regulation, College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, People’s Republic of China
2
College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, People’s Republic of China
3
School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221006, Jiangsu, People’s Republic of China
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. Keywords General regression neural network Interative algorithm Ordinary kriging Regression kriging Spatial prediction Soil organic matter
Introduction Soil organic matter (SOM) is a major soil property with a strong impact on physical, chemical, and biological processes, primarily fertility and plant growth. Accurate estimation of SOM variability would provide critical information on nutrient cycling and sediment (Guo et al. 2013). An improved understanding of SOM contents and spatial variability is necessary
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