Application of Artificial Neural Network in Environmental Water Quality Assessment
Application of Artificial Neural Network in Environmental Water Quality Assessment
复制标题
人工神经网络在环境水质评价中的应用
DOI：
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复制
发表时间：
2013-01
影响因子：
1.2
通讯作者：
Zhang, L.
中科院分区：
文献类型：
--
作者：
Chu, H. B.;Lu, W. X.;Zhang, L.
水质评价为水资源开发管理提供科学依据。本案例研究提出了基于因子分析方法和Hopfield神经网络方法的因子分析-Hopfield神经网络模型。结果表明，引入因子分析(FA)技术可以识别出重要的水质参数。结果表明，生化需氧量、高锰酸盐指数、氨氮、氮、铜、锌、铅是评价研究区水质变化的最重要参数。考虑到这些参数，将采样点的水样划分为：6个为III类，8个为IV类，6个为V类。然后，根据因子分析-Hopfield神经网络模型的水质评价结果绘制了水质图。结果表明，研究区西南部水质总体较好，东北部水质严重退化。因子分析-Hopfield神经网络在有效降低由输入引起的Hopfield神经网络过拟合度方面明显优于Hopfield神经网络，从而得到更合理的结果。与BP神经网络、模糊评价法和内梅罗指数法的比较表明，FHNN模型比其他三种水质分类方法提供了更可靠的判断和更有价值的信息。
Water quality assessment provides a scientific basis for water resources development and management. This case study proposes a Factor analysis- Hopfield neural network model (FHNN) based on factor analysis method and Hopfield neural network method. The results showed that the factor analysis (FA) technique was introduced to identify important water quality parameters. Results revealed that biochemical oxygen demand, permanganate index, ammonia nitrogen, nitrogen, Cu, Zn and Pb were the most important parameters in assessing water quality variations of the study area. Considering these parameters, water samples of the sampling sites were classified as follows: six into Class III, eight into Class IV, and six into Class V. Afterwards, a water quality map was based on the results of water quality assessment by Factor analysis-Hopfield neural network model. It showed that the southwestern part of the study area had a generally optimum water quality, while in the northeastern part, the quality was seriously degraded. Factor Analysis-Hopfield Neural Network was much better than the Hopfield Neural Network in effectively reducing the degree of Hopfield neural network over-fitting caused by the inputs, thereby achieving more reasonable results. The comparisons with BPANN, fuzzy assessment method, and the Nemerow index method indicated that the FHNN model provided more reliable judgment and valuable information than the three other water quality classification methods.
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DOI：
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发表时间：
2002
期刊：
Journal of Chongqing Jianzhu University
影响因子：
--
作者：
L. Teng
通讯作者：
L. Teng
影响因子：
8.7
作者：
A. Erturk;M. Gurel;A. Ekdal;Cigdem Tavsan;Aysegul Ugurluoglu;D. Seker;A. Tanik;I. Ozturk
通讯作者：
A. Erturk;M. Gurel;A. Ekdal;Cigdem Tavsan;Aysegul Ugurluoglu;D. Seker;A. Tanik;I. Ozturk
影响因子：
3.1
作者：
Cüneyt Karul;S. Soyupak;A. Çilesiz;N. Akbay;Emin Germen
通讯作者：
Cüneyt Karul;S. Soyupak;A. Çilesiz;N. Akbay;Emin Germen
DOI：
10.1007/s11270-009-0179-5
发表时间：
2010-06
期刊：
Water, Air, & Soil Pollution
影响因子：
--
作者：
K. Moustris;I. Ziomas;A. Paliatsos
通讯作者：
K. Moustris;I. Ziomas;A. Paliatsos
影响因子：
1.2
作者：
K. Oliveira-Esquerre;M. Mori;R. Bruns
通讯作者：
K. Oliveira-Esquerre;M. Mori;R. Bruns