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

DOI

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering
PB  - Atlantis Press
SP  - 678
EP  - 683
SN  - 1951-6851
UR  - https://doi.org/10.2991/amcce-15.2015.198
DO  - 10.2991/amcce-15.2015.198
ID  - Wu2015/04
ER  -