Title :
Soft sensing of seawater chlorophyll-a based on support vector machine algorithm
Author :
Zhang Ying ; Shi Jia ; Lu Li
Author_Institution :
Coll. of Inf. Eng., Shanghai Maritime Univ., Shanghai, China
Abstract :
Recently with the rapid developing of economy, the problem of seawater eutrophication due to pollution of marine environment becomes more serious. Seawater eutrophication will lead to more reproduction of algae in seawater, the reproduction state of algae can be obtained by monitoring the concentration of seawater chlorophyll-a. A soft sensing method of measuring the concentration of seawater chlorophyll-a based on Support Vector Machines(SVM) has been proposed in this paper. Cross-validation method was used for searching the optimal parameters of SVM model. The result of soft sensing based on SVM has been compared with the way of T-S fuzzy neural network, the testing result indicates that this soft sensing method based on SVM can effectively estimate the concentration of seawater chlorophyll-a, and then perform the real time state monitoring of seawater eutrophication.
Keywords :
fuzzy neural nets; geophysics computing; marine pollution; microorganisms; oceanographic techniques; seawater; support vector machines; T-S fuzzy neural network; algae reproduction state; cross-validation method; marine environment pollution; optimal parameters; real time state monitoring; seawater chlorophyll-a concentration; seawater eutrophication; soft sensing method; support vector machine algorithm; Abstracts; Algae; Educational institutions; Electronic mail; Monitoring; Sensors; Support vector machines; Chlorophyll-a; Cross-validation method; Sample learning; Soft sensing; Support Vector Machines;
Conference_Titel :
Control Conference (CCC), 2013 32nd Chinese
Conference_Location :
Xi´an
