Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm

Rong Chen; Shishi Liu; Yufei Yang; Wei Huang; Zongwei Han; Peihong Fu

doi:doi:10.11648/j.earth.20190806.14

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Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm

Rong Chen, Shishi Liu, Yufei Yang, Wei Huang, Zongwei Han, Peihong Fu

Published in Earth Sciences (Volume 8, Issue 6)

Received: 27 September 2019 Accepted: 6 November 2019 Published: 22 November 2019

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Abstract

In this study, the spatial distribution pattern of the roads, historical samples, digital elevation data, and other available resources were incorporated into the design of a soil-sampling scheme to predict the soil organic matter (SOM) of the northern region of Zhongxiang City, Hubei Province, and simulated annealing (SA) was applied to optimize the sampling design. The sampling points determined after optimization were used to establish a multivariate linear regression model to adequately reproduce the intrinsic link between topographic factors and the SOM at 13 different sampling scales in areas nearby the existing roadways in the study area. The topographic factors included slope, plane curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), and sediment transport index (STI). A multilayer perceptron (MLP) model was also constructed. Comparison of the accuracy of the multivariate linear regression and MLP models demonstrated the feasibility of an optimized soil sampling design based on the road network. With the optimized sampling design, accurate soil-landscape information can be obtained, and its precision is greater than that of the original sampling scheme before optimization. The optimized sampling design obtained reduces sampling costs, increases sampling efficiency, and provides an effective method for obtaining the spatial distribution pattern of organic matter in soils.

Published in	Earth Sciences (Volume 8, Issue 6)
DOI	10.11648/j.earth.20190806.14
Page(s)	335-345
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Soil-landscape Model, Simulated Annealing (SA), Multilayer Perceptron (MLP), Sampling Design Optimization

References

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APA Style

Rong Chen, Shishi Liu, Yufei Yang, Wei Huang, Zongwei Han, et al. (2019). Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm. Earth Sciences, 8(6), 335-345. https://doi.org/10.11648/j.earth.20190806.14

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ACS Style

Rong Chen; Shishi Liu; Yufei Yang; Wei Huang; Zongwei Han, et al. Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm. Earth Sci. 2019, 8(6), 335-345. doi: 10.11648/j.earth.20190806.14

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AMA Style

Rong Chen, Shishi Liu, Yufei Yang, Wei Huang, Zongwei Han, et al. Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm. Earth Sci. 2019;8(6):335-345. doi: 10.11648/j.earth.20190806.14

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@article{10.11648/j.earth.20190806.14,
  author = {Rong Chen and Shishi Liu and Yufei Yang and Wei Huang and Zongwei Han and Peihong Fu},
  title = {Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm},
  journal = {Earth Sciences},
  volume = {8},
  number = {6},
  pages = {335-345},
  doi = {10.11648/j.earth.20190806.14},
  url = {https://doi.org/10.11648/j.earth.20190806.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.earth.20190806.14},
  abstract = {In this study, the spatial distribution pattern of the roads, historical samples, digital elevation data, and other available resources were incorporated into the design of a soil-sampling scheme to predict the soil organic matter (SOM) of the northern region of Zhongxiang City, Hubei Province, and simulated annealing (SA) was applied to optimize the sampling design. The sampling points determined after optimization were used to establish a multivariate linear regression model to adequately reproduce the intrinsic link between topographic factors and the SOM at 13 different sampling scales in areas nearby the existing roadways in the study area. The topographic factors included slope, plane curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), and sediment transport index (STI). A multilayer perceptron (MLP) model was also constructed. Comparison of the accuracy of the multivariate linear regression and MLP models demonstrated the feasibility of an optimized soil sampling design based on the road network. With the optimized sampling design, accurate soil-landscape information can be obtained, and its precision is greater than that of the original sampling scheme before optimization. The optimized sampling design obtained reduces sampling costs, increases sampling efficiency, and provides an effective method for obtaining the spatial distribution pattern of organic matter in soils.},
 year = {2019}
}

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TY  - JOUR
T1  - Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm
AU  - Rong Chen
AU  - Shishi Liu
AU  - Yufei Yang
AU  - Wei Huang
AU  - Zongwei Han
AU  - Peihong Fu
Y1  - 2019/11/22
PY  - 2019
N1  - https://doi.org/10.11648/j.earth.20190806.14
DO  - 10.11648/j.earth.20190806.14
T2  - Earth Sciences
JF  - Earth Sciences
JO  - Earth Sciences
SP  - 335
EP  - 345
PB  - Science Publishing Group
SN  - 2328-5982
UR  - https://doi.org/10.11648/j.earth.20190806.14
AB  - In this study, the spatial distribution pattern of the roads, historical samples, digital elevation data, and other available resources were incorporated into the design of a soil-sampling scheme to predict the soil organic matter (SOM) of the northern region of Zhongxiang City, Hubei Province, and simulated annealing (SA) was applied to optimize the sampling design. The sampling points determined after optimization were used to establish a multivariate linear regression model to adequately reproduce the intrinsic link between topographic factors and the SOM at 13 different sampling scales in areas nearby the existing roadways in the study area. The topographic factors included slope, plane curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), and sediment transport index (STI). A multilayer perceptron (MLP) model was also constructed. Comparison of the accuracy of the multivariate linear regression and MLP models demonstrated the feasibility of an optimized soil sampling design based on the road network. With the optimized sampling design, accurate soil-landscape information can be obtained, and its precision is greater than that of the original sampling scheme before optimization. The optimized sampling design obtained reduces sampling costs, increases sampling efficiency, and provides an effective method for obtaining the spatial distribution pattern of organic matter in soils.
VL  - 8
IS  - 6
ER  -

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Author Information

Rong Chen

College of Resource and Environment, Huazhong Agricultural University, Wuhan, China
Shishi Liu

College of Resource and Environment, Huazhong Agricultural University, Wuhan, China
Yufei Yang

College of Resource and Environment, Huazhong Agricultural University, Wuhan, China
Wei Huang

College of Resource and Environment, Huazhong Agricultural University, Wuhan, China
Zongwei Han

Department of Tourism and Geography, Tongren University, Tongren, China
Peihong Fu

College of Resource and Environment, Huazhong Agricultural University, Wuhan, China

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Plain Text BibTeX RIS

APA Style

Rong Chen, Shishi Liu, Yufei Yang, Wei Huang, Zongwei Han, et al. (2019). Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm. Earth Sciences, 8(6), 335-345. https://doi.org/10.11648/j.earth.20190806.14

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ACS Style

Rong Chen; Shishi Liu; Yufei Yang; Wei Huang; Zongwei Han, et al. Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm. Earth Sci. 2019, 8(6), 335-345. doi: 10.11648/j.earth.20190806.14

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AMA Style

Rong Chen, Shishi Liu, Yufei Yang, Wei Huang, Zongwei Han, et al. Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm. Earth Sci. 2019;8(6):335-345. doi: 10.11648/j.earth.20190806.14

Copy | Download

@article{10.11648/j.earth.20190806.14,
  author = {Rong Chen and Shishi Liu and Yufei Yang and Wei Huang and Zongwei Han and Peihong Fu},
  title = {Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm},
  journal = {Earth Sciences},
  volume = {8},
  number = {6},
  pages = {335-345},
  doi = {10.11648/j.earth.20190806.14},
  url = {https://doi.org/10.11648/j.earth.20190806.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.earth.20190806.14},
  abstract = {In this study, the spatial distribution pattern of the roads, historical samples, digital elevation data, and other available resources were incorporated into the design of a soil-sampling scheme to predict the soil organic matter (SOM) of the northern region of Zhongxiang City, Hubei Province, and simulated annealing (SA) was applied to optimize the sampling design. The sampling points determined after optimization were used to establish a multivariate linear regression model to adequately reproduce the intrinsic link between topographic factors and the SOM at 13 different sampling scales in areas nearby the existing roadways in the study area. The topographic factors included slope, plane curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), and sediment transport index (STI). A multilayer perceptron (MLP) model was also constructed. Comparison of the accuracy of the multivariate linear regression and MLP models demonstrated the feasibility of an optimized soil sampling design based on the road network. With the optimized sampling design, accurate soil-landscape information can be obtained, and its precision is greater than that of the original sampling scheme before optimization. The optimized sampling design obtained reduces sampling costs, increases sampling efficiency, and provides an effective method for obtaining the spatial distribution pattern of organic matter in soils.},
 year = {2019}
}

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TY  - JOUR
T1  - Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm
AU  - Rong Chen
AU  - Shishi Liu
AU  - Yufei Yang
AU  - Wei Huang
AU  - Zongwei Han
AU  - Peihong Fu
Y1  - 2019/11/22
PY  - 2019
N1  - https://doi.org/10.11648/j.earth.20190806.14
DO  - 10.11648/j.earth.20190806.14
T2  - Earth Sciences
JF  - Earth Sciences
JO  - Earth Sciences
SP  - 335
EP  - 345
PB  - Science Publishing Group
SN  - 2328-5982
UR  - https://doi.org/10.11648/j.earth.20190806.14
AB  - In this study, the spatial distribution pattern of the roads, historical samples, digital elevation data, and other available resources were incorporated into the design of a soil-sampling scheme to predict the soil organic matter (SOM) of the northern region of Zhongxiang City, Hubei Province, and simulated annealing (SA) was applied to optimize the sampling design. The sampling points determined after optimization were used to establish a multivariate linear regression model to adequately reproduce the intrinsic link between topographic factors and the SOM at 13 different sampling scales in areas nearby the existing roadways in the study area. The topographic factors included slope, plane curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), and sediment transport index (STI). A multilayer perceptron (MLP) model was also constructed. Comparison of the accuracy of the multivariate linear regression and MLP models demonstrated the feasibility of an optimized soil sampling design based on the road network. With the optimized sampling design, accurate soil-landscape information can be obtained, and its precision is greater than that of the original sampling scheme before optimization. The optimized sampling design obtained reduces sampling costs, increases sampling efficiency, and provides an effective method for obtaining the spatial distribution pattern of organic matter in soils.
VL  - 8
IS  - 6
ER  -

Copy | Download