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 ...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 a 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 C=64 and the kernel parameter y=l, 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.展开更多
基金Supported by the National Key Research and Development Program(No.2016YFC1402101)the National Natural Science Foundation of China(No.41376106)the Natural Science Foundation of Shandong Province(No.ZR2013DM017)
文摘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 a 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 C=64 and the kernel parameter y=l, 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.
