Elman神经网络及其在河口水质评价中的应用  

Elman Neural Network and its Application in Estuarine Water Quality Assessment

作　　者：范翠香[1] 张园园 薛鹏松 

机构地区：[1]西安理工大学高等技术学院,西安710048 [2]西安联能自动化工程有限责任公司,西安710119

出　　处：《计算机系统应用》2015年第3期251-255,共5页Computer Systems & Applications

基　　金：高等学校博士学科点专项科研基金博导类资助课题(20126118110015)

摘　　要：应用Elman神经网络对河口水质进行评价,确定其水质级别及污染程度.根据汾河入黄口的实际污染情况及因子选择的目的原则,确定评价因子,构建基于Elman神经网络的河口水质评价模型.应用训练好的Elman神经网络河口水质评价模型对河津大桥监测断面2010年各月水质进行评价,分析研究汾河入黄口处的水质污染状况,结果表明,汾河入黄口河津大桥监测断面2010年各月综合水质均为劣Ⅴ类水,因此,汾河入黄口污染治理迫在眉睫,应从源头加强汾河污染物入河量的控制.水质识别实例表明Elman河口水质评价模型避免了传统神经网络无法实时改变模型结构和缺乏对未来突变情况适应性的缺点,使得训练好的网络具有非线性和动态特性,水质评价结果切合实际,具有很好的实用性.Elman neural network was applied to evaluate estuarine water quality, and then the water quality and pollution levels were determined. According to the actual pollution of Fen River＇s estuary to Yellow River and the objective principle of factor selection, the evaluation factors were determined, and the estuarine water quality evaluation model which was based on Elman neural network was established. The trained model was used to evaluate the water quality of Hejin bridge monitoring section each month in 2010 and analyse the water pollution condition of Fen River＇s estuary to Yellow River. Results indicated that the comprehensive water quality of Hejin bridge monitoring section at Fen River＇s estuary to Yellow River each month in 2010 were inferior Ⅴ. Therefore, the pollution control of Fen River＇s estuary to Yellow River is imminent, source control of pollutants into Fen River should be strengthened. The example of water quality identify shows that the model can avoid the shortcomings of traditional neural network model, such as traditional neural network model cannot change the structure of the model in real time and it lacks of adaptability to future mutations, and make the trained network with nonlinear and dynamic characteristics. The water quality evaluation results of this model are realistic. So, the model has a good usability.

关 键 词：ELMAN神经网络 汾河入黄口 水质评价 评价因子 污染物 

分 类 号：X824[环境科学与工程—环境工程]