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Real-time eutrophication status evaluation of coastal waters using support vector machine with grid search algorithm.

Abstract：

:The development of techniques for real-time monitoring of the eutrophication status of coastal waters is of great importance for realizing potential cost savings in coastal monitoring programs and providing timely advice for marine health management. In this study, a GS optimized SVM was proposed to model relationships between 6 easily measured parameters (DO, Chl-a, C1, C2, C3 and C4) and the TRIX index for rapidly assessing marine eutrophication states of coastal waters. The good predictive performance of the developed method was indicated by the R2 between the measured and predicted values (0.92 for the training dataset and 0.91 for the validation dataset) at a 95% confidence level. The classification accuracy of the eutrophication status was 86.5% for the training dataset and 85.6% for the validation dataset. The results indicated that it is feasible to develop an SVM technique for timely evaluation of the eutrophication status by easily measured parameters.

journal_name

Mar Pollut Bull

journal_title

Marine pollution bulletin

authors

Kong X,Sun Y,Su R,Shi X

doi

10.1016/j.marpolbul.2017.04.022

subject

Has Abstract

pub_date

2017-06-15 00:00:00

pages

307-319

issue

1

eissn

0025-326X

issn

1879-3363

pii

S0025-326X(17)30329-6

journal_volume

119

pub_type

杂志文章

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