Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm
Earth Sciences
Volume 8, Issue 6, December 2019, Pages: 335-345
Received: Sep. 27, 2019; Accepted: Nov. 6, 2019; Published: Nov. 22, 2019
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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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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.
Soil-landscape Model, Simulated Annealing (SA), Multilayer Perceptron (MLP), Sampling Design Optimization
To cite this article
Rong Chen, Shishi Liu, Yufei Yang, Wei Huang, Zongwei Han, Peihong Fu, Optimization of Soil Sampling Design Based on Road Networks – A Simulated Annealing/Neural Network Algorithm, Earth Sciences. Vol. 8, No. 6, 2019, pp. 335-345. doi: 10.11648/j.earth.20190806.14
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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