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
相关论文
共 27 条
  • [1] Ai X, 2017, COGENT ENG, V4, DOI 10.1080/23311916.2017.1360236
  • [2] Investigating the role for adaptation of the microbial community to transform trace organic chemicals during managed aquifer recharge
    Alidina, Mazahirali
    Li, Dong
    Drewes, Joerg E.
    [J]. WATER RESEARCH, 2014, 56 : 172 - 180
  • [3] Exploring corporate lobbyists' perceptions of prospective coalition partners in Brussels
    Barron, Andrew
    Hulten, Peter
    [J]. ENVIRONMENT AND PLANNING C-GOVERNMENT AND POLICY, 2014, 32 (06): : 963 - 981
  • [4] Cao Y, 2015, IEEE ANN INT CONF CY, P1486, DOI 10.1109/CYBER.2015.7288164
  • [5] Phytoremediation of palm oil mill secondary effluent (POMSE) by Chrysopogon zizanioides (L.) using artificial neural networks
    Darajeh, Negisa
    Idris, Azni
    Masoumi, Hamid Reza Fard
    Nourani, Abolfazl
    Truong, Paul
    Rezania, Shahabaldin
    [J]. INTERNATIONAL JOURNAL OF PHYTOREMEDIATION, 2017, 19 (05) : 413 - 424
  • [6] Annual Precipitation and Effects of Runoff Nutrient From Agricultural Watersheds on Water Quality
    Elrashidi, Moustafa A.
    Seybold, Cathy A.
    Delgado, Jorge
    [J]. SOIL SCIENCE, 2013, 178 (12) : 679 - 688
  • [7] [冯仲恺 Feng Zhongkai], 2015, [水科学进展, Advances in Water Science], V26, P413
  • [8] Ghaith S., 2014, CONF PERFORMANCE ENG, P273
  • [9] Toward appropriate criteria in medication adherence assessment in older persons: Position Paper
    Giardini, Anna
    Teresa Martin, Maria
    Cahir, Caitriona
    Lehane, Elaine
    Menditto, Enrica
    Strano, Maria
    Pecorelli, Sergio
    Monaco, Alessandro
    Marengoni, Alessandra
    [J]. AGING CLINICAL AND EXPERIMENTAL RESEARCH, 2016, 28 (03) : 371 - 381
  • [10] Low Temperature Synthesis of MnO2/Graphene Nanocomposites for Supercapacitors
    Huang, Hao
    Sun, Guangren
    Hu, Jie
    Jiao, Tifeng
    [J]. JOURNAL OF CHEMISTRY, 2015, 2015