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《Environmental Science & Technology》 2012-01
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SVM and ANN Applied to Evaluation of Lake Eutrophication:A Comparative Study

YANG Dao-jun1,WANG Ran2,SHEN Gang1(1.Maanshan Institute of Mining Research of Sinosteel Co.Ltd,Maanshan 243000,China; 2.Maanshan Institute of Environmental Sciences,Maanshan 243000,China)  
Support Vector Machine(SVM),a new statistic-learning-theory based method proposed by V.Vapnik,can be used to tackle non-linear problems owing to its kernel function,i.e.,structural risk minimization principle instead of the experimental risk minimization.Artificial neural network(ANN) has been applied for years to resolving the problems of pattern recognition and regression,but some questions still remained unresolved.Based on the water quality data from 2001 to 2003 of Chaohu Lake,with which evaluations of the Lake's eutrophication level were performed by use of both SVM and ANN,this paper gave a result of this comparative study that indicated that SVM was more practical in describing the lake's eutrophication status.
【CateGory Index】: X824
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