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Application Research of Artificial Neural Network in Environmental Quality Monitoring.

Authors :
Zhao, Kunrong
He, Tingting
Wu, Shuang
Wang, Songling
Dai, Bilan
Yang, Qifan
Lei, Yutao
Source :
International Journal of Pattern Recognition & Artificial Intelligence; Nov2019, Vol. 33 Issue 12, pN.PAG-N.PAG, 18p
Publication Year :
2019

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02180014
Volume :
33
Issue :
12
Database :
Complementary Index
Journal :
International Journal of Pattern Recognition & Artificial Intelligence
Publication Type :
Academic Journal
Accession number :
139861257
Full Text :
https://doi.org/10.1142/S0218001419590390