A new technique for rapid assessment of eutrophication status of coastal waters using a support vector machine

Xianyu KONG; Xiaowei CHE; Rongguo SU; Chuansong ZHANG; Qingzhen YAO; Xiaoyong SHI

doi:10.1007/s00343-017-6224-0

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A new technique for rapid assessment of eutrophication status of coastal waters using a support vector machine

Ecology | Updated：2023-05-19

- A new technique for rapid assessment of eutrophication status of coastal waters using a support vector machine
- Chinese Journal of Oceanology and Limnology Vol. 36, Issue 2, Pages: 249-262(2018)
- Affiliations：
  
  Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, Qingdao 266100, China
- Author bio：
  
  SU Rongguo, surongguo@ouc.edu.cn
- Funds：
- DOI：10.1007/s00343-017-6224-0
  CLC：
- Received：19 September 2016，
  
  Accepted：12 December 2016，
  
  Online First：04 January 2017，
  
  Published：2018-03
- Accepted：
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Xianyu KONG, Xiaowei CHE, Rongguo SU, et al. A new technique for rapid assessment of eutrophication status of coastal waters using a support vector machine[J]. Chinese Journal of Oceanology and Limnology, 2018, 36(2): 249-262.
DOI：

Xianyu KONG, Xiaowei CHE, Rongguo SU, et al. A new technique for rapid assessment of eutrophication status of coastal waters using a support vector machine[J]. Chinese Journal of Oceanology and Limnology, 2018, 36(2): 249-262. DOI： 10.1007/s00343-017-6224-0.

摘要

Abstract

There is an urgent need to develop efficient evaluation tools that use easily measured variables to make rapid and timely eutrophication assessments

which are important for marine health management

and to implement eutrophication monitoring programs. In this study

an approach for rapidly assessing the eutrophication status of coastal waters with three easily measured parameters (turbidity

chlorophyll

and dissolved oxygen) was developed by the grid search (GS) optimized support vector machine (SVM)

with trophic index TRIX classification results as the reference. With the optimized penalty parameter

=64 and the kernel parameter

the classification accuracy rates reached 89.3% for the training data

88.3% for the cross-validation

and 88.5% for the validation dataset. Because the developed approach only used three easy-to-measure variables

its application could facilitate the rapid assessment of the eutrophication status of coastal waters

resulting in potential cost savings in marine monitoring programs and assisting in the provision of timely advice for marine management.

关键词

Keywords

references

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