Application Research of Artificial Neural Network in Environmental Quality Monitoring

被引:0
|
作者
Zhao, Kunrong [1 ]
He, Tingting [2 ]
Wu, Shuang [3 ]
Wang, Songling [1 ]
Dai, Bilan [1 ]
Yang, Qifan [2 ]
Lei, Yutao [1 ]
机构
[1] South China Inst Environm Sci, MEP, Guangzhou, Guangdong, Peoples R China
[2] Guangzhou Hexin Environm Protect Technol Co Ltd, Guangzhou, Guangdong, Peoples R China
[3] Guangzhou Huake Environm Protect Engn Co Ltd, Minist Environm Protect, South China Inst Environm Sci, Guangzhou, Guangdong, Peoples R China
关键词
Artificial neural network; BP; fuzzy neural network; environmental quality; environmental monitoring; MODEL;
D O I
10.1142/S0218001419590390
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the steady growth of the economy and the rapid development of modern industrial technology, the problem of environmental pollution has increased. To continue to develop, it is necessary to thoroughly implement the sustainable development strategy, and we must pay more attention to environmental issues. One of the important management tools implemented in China for environmental management is environmental quality monitoring and evaluation. Environmental quality monitoring can scientifically evaluate the environmental quality of a region, scientifically evaluate and forecast the environmental management and environmental engineering, and provide scientific basis for environmental management, environmental engineering, formulation of environmental standards, environmental planning, comprehensive prevention and control of environmental pollution, and ecological environment construction. This paper will discuss the basic principles of neural network and the implementation process of MATLAB and in the MATLAB software implementation and display process. At the same time, the results of different parameters are analyzed through experiments, and the network parameters are constantly adjusted to improve the accuracy of the evaluation results. Taking the regional environment as an example, two monitoring methods are proposed, and a variety of neural network models are used to analyze each prediction method. Case study results show that the latter method has a better prediction effect.
引用
收藏
页数:18
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