Soft Sensing for the State of Algae Growth in Seawater Based on Support Vector Machine Algorithm

ZHANG Ying; SHI Jia

doi:10.3969/j.issn.0254-0037.2014.07.004

Journal of Beijing University of Technology > 2014 > 40(7): 980-985. > DOI: 10.3969/j.issn.0254-0037.2014.07.004

ZHANG Ying, SHI Jia. Soft Sensing for the State of Algae Growth in Seawater Based on Support Vector Machine Algorithm[J]. Journal of Beijing University of Technology, 2014, 40(7): 980-985. DOI: 10.3969/j.issn.0254-0037.2014.07.004

Citation:

PDF (854 KB)

Soft Sensing for the State of Algae Growth in Seawater Based on Support Vector Machine Algorithm

ZHANG Ying,
SHI Jia

College of Information Engineering,Shanghai Maritime University, Shanghai 201306, China

More Information

Received Date: May 09, 2013
Available Online: January 10, 2023

Graphical Abstract

Abstract

Abstract

To effectively monitor the state of algae growth in seawater, a method of soft sensing for the key representative factor based on support vector machine （SVM) algorithm was investigated. First, gridding optimization was adopted to optimize the penalty factor C and parameter σ of SVM. Then, the optimal matching parameters were used to obtain the soft sensing model of concentration of seawater chlorophyll-a via the samples training. The result of soft sensing based on SVM was compared with the result of using BP neural network. The testing result shows that the soft sensing method based on SVM has more prediction accuracy and stability than the method of BP neural network. The SVM model can be used in soft sensing for the state of seawater algae growth.
- support vector machines algorithm,
- algae growth state,
- parameters optimization,
- soft sensing,
- neural networks

FullText(HTML)

References (13)

References

[1]	王洪礼, 葛根, 李悦雷.基于模糊神经网络 (FNN) 的赤潮预警预测研究[J].海洋通报, 2006, 25 (4) :36-41.WANG Hong-li, GE gen, LI Yue-lei.Research on the prediction of red tide based on the fuzzy neural network[J].Marine Science Bulletin, 2006, 25 (4) :36-41. (in Chinese)
[2]	MELESSE A M, KRISHNASWAMY J, ZHANG K Q.Modeling coastal eutrophication at Florida Bay using neural networks[J].Journal of Coastal Research, 2008, 24 (2) :190-196.
[3]	张丽旭, 张小伟.用于海洋环境科学的一种新方法——影响因子分析法[J].海洋科学进展.2004, 22 (1) :33-36.ZHANG Li-xu, ZHANG Xiao-wei.Used in marine environmental science, a new method—impact factor analysis method[J].Advances in Marine Science, 2004, 22 (1) :33-36. (in Chinese)
[4]	SUSANA A, CARLOS M D.Phytoplankton chlorophyll-a distribution and water column stability in the central Atlantic Ocean[J].Oceanologica Acta, 1999, 22 (2) :193-203.
[5]	李雪, 刘长发, 朱学慧, 等.基于BP人工神经网络的海水水质综合评价[J].海洋通报.2010, 29:225-230.LI Xue, LIU Chang-fa, ZHU Xue-hui, et al.The comprehensive evaluation of water quality based on BP artificial neural network[J].Marine Science Bulletin, 2010, 29:225-230. (in Chinese)
[6]	LEE J H W, HUANG Y, DICKMAN M, et al.Neural network modelling of coastal algal blooms[J].Ecological Modelling, 2003, 159 (2) :179-201.
[7]	VAPNIK V, CHAPELLE O.Bounds on error expectation for support vector machines[J].Neural Computation, 2000, 12 (9) :2013-2036.
[8]	SCHOLKOPF B, SMOLA A J.Learning with kernels[M].Cambridge:MIT Press, 2002:23-78.
[9]	LIU Z W, WANG X Y, CUI L F, et al.Research on water bloom prediction based on least squares support vector machine[C]//2009 World Congress on Computer Science and Information Engineering.Los Angeles:IEEE, 2009:764-768.
[10]	CHUANG C C, LEE Z J.Hybrid robust support vector machines for regression with outliers[J].Applied Soft Computing, 2011, 11:64-72.
[11]	刘润幸, 萧灿培, 宫齐, 等.利用SPSS进行主成分回归分析[J].数理医药学杂志, 2001, 14 (2) :103-105.LIU Run-xin, XIAO Can-pei, GONG Qi, et al.SPSS principal component regression analysis[J].Journal of Mathematical Medicine, 2001, 14 (2) :103-105. (in Chinese)
[12]	李琳, 张晓龙.基于RBF核的SVM学习算法的优化计算[J].计算机工程与应用, 2006, 29:190-192.LI Lin, ZHANG Xiao-long.Optimization of SVM with RBF kernel[J].Computer Engineering and Applications, 2006, 29:190-192. (in Chinese)
[13]	NASON G P.Wavelet shrinkage using cross validation[J].Journal of the Royal Statistical Society, Series B:Methodological, 1996, 58 (2) :463-479.

Cited By

Get Citation

PDF

XML

Article views (12) PDF downloads (9)

Turn off MathJax

Article Contents

Abstract

References

Soft Sensing for the State of Algae Growth in Seawater Based on Support Vector Machine Algorithm

Abstract

References

Catalog

Export File

Citation

Format

Content