pp 1–11 | Cite as

Simulating Spatial Variation of Soil Carbon Content in the Yellow River Delta: Comparative Analysis of Two Artificial Neural Network Models

  • Chen Wang
  • Yuan Cui
  • Ziwen Ma
  • Yutong Guo
  • Qian Wang
  • Yujiao Xiu
  • Rong XiaoEmail author
  • Mingxiang ZhangEmail author
General Wetland Science


A reasonable predicting model for spatial variation of soil carbon would be a useful tool in monitoring and restoration of salt marshes. In this study, radial basis function neural networks model (RBFNN) and back propagation neural networks model (BPNN) were built to predict total carbon (TC), total organic carbon (TOC) and dissolved organic carbon (DOC) contents in salt marsh of the Yellow River Delta. Both models contained thirteen input parameters, i.e., nine topographic factors selected from ASTER GDEM Version2 and Geographical Information System (GIS), one vegetation index – MODIS 16-day composite Enhanced Vegetation Index (EVI), and three soil physicochemical properties. For prediction of TC, the MAE, MSE and RMSE values of RBFNN were smaller than those of BPNN by 61.87%, 81.36% and 56.82%; for TOC, the MAE, MSE and RMSE values of RBFNN were smaller than those of BPNN by 37.13%, 58.06% and 35.23%; both models had no significant difference in accuracy for DOC prediction, but the MAE, MSE and RMSE values of RBFNN were smaller. All ME values of RBFNN rather than BPNN were closer to zero. RBFNN integrating environmental factors had a higher accuracy than BPNN in predicting soil carbon content at a relatively small regional scale.


Soil carbon simulations RBFNN BPNN Regional scale Yellow River Delta 



artificial neural network


radial basis function neural networks model


back propagation neural networks model


total carbon


total organic carbon


dissolved organic carbon


electric conductivity


Yellow River delta


Digital Elevation Model


topographic wetness index


relief amplitude


slope of slope


surface roughness


slope length and steepness factor


plan curvature


profile curvature


Geographical Information System


Enhanced Vegetation Index


mean absolute error


mean squared Error


root mean squared error


mean error



This study was financially supported by the National Natural Science Foundation of China (51609005), the National Key R&D Program of China (2017YFC0505903) and the Fundamental Research Fund for the Central Universities (2015ZCQ-BH-01). The authors gratefully acknowledge Profs. Baoshan Cui and Junhong Bai, and lab members Lidi Zheng, Jingxiao Chen, Zhuoqun Wei, Yueyan Pan and the anonymous reviewers for their great helps and valuable suggestions.


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Copyright information

© Society of Wetland Scientists 2019

Authors and Affiliations

  1. 1.School of Nature ConservationBeijing Forestry UniversityBeijingPeople’s Republic of China
  2. 2.College of Environment and ResourcesFuzhou UniversityFuzhouPeople’s Republic of China

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