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

MARINE POLLUTION BULLETIN

卷:119

Article

Kong, Xianyu¹  Sun, Yuyan¹  Su, Rongguo¹  Shi, Xiaoyong¹ 

[1] Ocean Univ China, Minist Educ, Key Lab Marine Chem Theory & Technol, Qingdao 266100, Peoples R China

关键词: Eutrophication assessment;    Easily measured parameters;    CDOM;    Fluorescence;    TRIX;    Support vector machine;   

DOI  :  10.1016/j.marpolbul.2017.04.022

来源: Elsevier

【 摘 要 】

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 R-2 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.

【 授权许可】

Free