基于BP人工神经网络的海水水质综合评价  

Integrated assessment of sea water quality based on BP artificial neural network

作　　者：李雪[1] 刘长发[1,2] 朱学慧[1] 谢谢[1] 

机构地区：[1]大连海洋大学海洋环境工程学院,辽宁大连116023 [2]近岸海洋环境科学与技术辽宁省高校重点实验室,辽宁大连116023

出　　处：《海洋通报》2010年第2期225-230,共6页Marine Science Bulletin

基　　金：国家海洋公益性行业专项(200805069)

摘　　要：为了能够客观地对海水水质进行综合评价,在分析人工神经网络概念和原理的基础上,从阈值角度出发,通过对各类海水水质污染指标浓度生成样本的方法,生成了适用于BP人工神经网络模型训练的样本,并应用基于误差反向传播原理的前向多层神经网络,建立了用于海水水质评价的BP人工神经网络模型。将该模型用于渤海湾近岸海域水环境评价,通过模型的计算,得到该海域的水质类别。结果表明,2004-2007年,渤海湾近岸海域污染指标总体上在河流丰水期时比枯水期时高,2005年和2006年污染较为严重,2007年有所好转。经训练的评价模型应用于实例的评价结果表明,该模型设计合理、泛化能力强,对海水水质评价具有较好的客观性、通用性和实用性。In order to carry out an integrated assessment of sea water quality objectively,this paper based on the concept and principle of artificial neural network,generated appropriate training samples for BP artificial neural network model through the method of producing samples to the concentration of various pollution index of sea water quality from the viewpoint of threshold,established the BP artificial neural network model of sea water quality assessment using multi-layer neural network with error back-propagation algorithm. This model is used to assess water environment and obtain sea water quality categories of offshore area in Bohai Bay through calculating. The calculations show that the pollution index in river’s wet season is higher than that of the dry season from 2004 to 2007,and the pollution is particularly serious in 2005 and 2006,but a little better in 2007. The assessment results show that the model is reasonable in design and higher in generalization,meanwhile,is common,objective and practical to sea water quality assessment.

关 键 词：人工神经网络 海水水质 训练样本 连接权值 评价 

分 类 号：X820.2[环境科学与工程—环境工程]