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Wetlands

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

Abstract

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.

Keywords

Soil carbon simulations RBFNN BPNN Regional scale Yellow River Delta 

Abbreviations

ANN

artificial neural network

RBFNN

radial basis function neural networks model

BPNN

back propagation neural networks model

TC

total carbon

TOC

total organic carbon

DOC

dissolved organic carbon

EC

electric conductivity

YRD

Yellow River delta

DEM

Digital Elevation Model

TWI

topographic wetness index

RA

relief amplitude

SOS

slope of slope

SR

surface roughness

LS

slope length and steepness factor

PlanC

plan curvature

ProfC

profile curvature

GIS

Geographical Information System

EVI

Enhanced Vegetation Index

MAE

mean absolute error

MSE

mean squared Error

RMSE

root mean squared error

ME

mean error

Notes

Acknowledgments

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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