Method for Optimal Arrangement of Soil Sampling Based on Neural Networks and Genetic Algorithms
Zongshu Wu, Jiaoyan Ai, Chaobing Deng, Yajuan Cai, Zongming Wei
Available Online April 2015.
- https://doi.org/10.2991/amcce-15.2015.198How to use a DOI?
- Neural networks;Genetic algorithms;Soil sampling;Optimal arrangement
- 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.
- 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 -