Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering

Method for Optimal Arrangement of Soil Sampling Based on Neural Networks and Genetic Algorithms

Authors
Zongshu Wu, Jiaoyan Ai, Chaobing Deng, Yajuan Cai, Zongming Wei
Corresponding Author
Zongshu Wu
Available Online April 2015.
DOI
https://doi.org/10.2991/amcce-15.2015.198How to use a DOI?
Keywords
Neural networks;Genetic algorithms;Soil sampling;Optimal arrangement
Abstract
In order to explore a new way of optimization for soil sampling layout, in this paper,spatial distribution of heavy metal concentrations which is based on RBF neural network fitting was studied by genetic algorithm, corresponding network structure and algorithm flow were constructed, in addition the network and algorithm parameters settings were analyzed and a sampling experiment using this method was conducted in an abandoned mine located in a county of Guangxi. The results of optimizing the layout point prove that the number of the sampling points can be reduced by about a half under the premise of meeting the fitting accuracy,this will greatly reduce the cost of sampling and analysis and the redundancy of the data, and is expected to promote the relevant application in soil composition analysis and other related fields.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2015 International Conference on Automation, Mechanical Control and Computational Engineering
Part of series
Advances in Intelligent Systems Research
Publication Date
April 2015
ISBN
978-94-62520-64-6
ISSN
1951-6851
DOI
https://doi.org/10.2991/amcce-15.2015.198How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Zongshu Wu
AU  - Jiaoyan Ai
AU  - Chaobing Deng
AU  - Yajuan Cai
AU  - Zongming Wei
PY  - 2015/04
DA  - 2015/04
TI  - Method for Optimal Arrangement of Soil Sampling Based on Neural Networks and Genetic Algorithms
BT  - 2015 International Conference on Automation, Mechanical Control and Computational Engineering
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/amcce-15.2015.198
DO  - https://doi.org/10.2991/amcce-15.2015.198
ID  - Wu2015/04
ER  -