基于支持向量机的水质监测数据处理及状况识别与评价方法
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摘要
水质评价实际上是一个监测数据处理与状态估计、识别的过程,提出一种基于支持向量机的方法应用于水质评价,该方法依据决策二叉树多类分类的思想,构建了基于支持向量机的水环境质量状况识别与评价模型。以长江口的实际水质监测数据为例进行了实验分析,并与单因子方法及单个BP神经网络方法进行了比较分析。实验结果表明,运用该模型对长江口的实际水质监测数据进行的综合水质评价效果较好,且具有较高的实用价值。
The assessment of water quality is a fusion of multi-source information,state evaluation and identification.This paper introduces a method to assess the lake water quality eutrophication based on support vector machine.Using the idea of decision binary tree,it constitutes a assessment model of water quality of monitoring data based on SVM.This paper uses an experiment on the monitoring data of Yangtse River port and gives a comparison and analysis with single factor assessment method and the single BP neural network.The experiment shows the good effect of such model and have potential application value.
The assessment of water quality is a fusion of multi-source information,state evaluation and identification.This paper introduces a method to assess the lake water quality eutrophication based on support vector machine.Using the idea of decision binary tree,it constitutes a assessment model of water quality of monitoring data based on SVM.This paper uses an experiment on the monitoring data of Yangtse River port and gives a comparison and analysis with single factor assessment method and the single BP neural network.The experiment shows the good effect of such model and have potential application value.
引文
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[14]SVMTool LIBSVM[EB/OL].http://www.csie.ntu.edu.tw/~cjlin/libsvm/,2005.
[2]时红,孙新忠,范建华,等.水质分析方法与技术[M].北京:地震出版社,2001.
[3]S im eonov V,Stefanov S,Tsakovsk i S.Environm etrical Treatm ent ofW ater Quality Survey Data from Yangtse R iver[J].M ikroch im Acta,2000,134(1-2):15-21.
[4]Loke E,W arnaars E A,Jacobsen P,et a.l ArtificialNeuralNetworks asa Fool in Urban Storm Damage[J].W ater Science and Technology,1997,36(8-9):101-110.
[5]任黎,董增川,李少华.人工神经网络模型在太湖富营养化评价中的应用[J].河海大学学报(自然科学版),2004,32(2):147-150.
[6]林志贵,徐立中,黄凤辰,等.基于D-S理论的多源水质监测数据融合处理[J].计算机工程与应用,2004,40(10):3-5.
[7]Guo G D,L i S Z.Content-based Aud io C lassification and Retrievalby Support Vector Mach ines[J].IEEE Trans.on Neural Network,2003,14(1):209-215.
[8]L i S Z.Content-based C lassification and Retrieval of Aud io Using theNearest Feature L ine M ethod[J].IEEE Trans.on Speech Aud io Pro-cessing,2000,8(9):619-625.
[9]Vapn ik V.The Nature of Statistical Learn ing Theory[M].New York:Spring-Verlag,1995.
[10]张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1):32-42.
[11]Nello C ristian in i,John Shawe-Taylor.An Introduction to SupportVec-torMach ines and O ther Kernel-based Learn ing M ethods[M].Bei-jing:Pub lish ing House of E lectron ics Industry,2004.
[12]Knerr S.Net Computer:M ethods,Configuration and Application[M].New York:Springer,1990.
[13]王晓丹,王积勤.支持向量机研究与应用[J].空军工程大学学报(自然科学版),2004,5(3):49-55.
[14]SVMTool LIBSVM[EB/OL].http://www.csie.ntu.edu.tw/~cjlin/libsvm/,2005.
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