Аuthorization:
Login:
Password:
  















Detailed information

 

IMPROVEMENT OF EFFICIENCY IN VIOLATION CONTROL OF HARMFUL EMISSION STANDARDS BY MEANS OF PREDICTION USING ARTIFICIAL NEURAL NETWORK



The article focuses on urgency of activities in the area of ecological safety. The mechanism of detecting violations of ecological standards in harmful emission is described. The object is set to improve efficiency of toxic emission concentration control by means of prediction using neural networks. The current nature of the objective is justified. The documents with the required source data for reaching the set objective are presented. The advantages of this approach are described. The formula is given for checking the data in order that the proposed method is efficient. The parameters, scheme, setting if learning parameters and the structure of the selected neural network are presented. A variant of interpreting the obtained data is discussed. The conclusion on the relevance of the objective is drawn.

: 1
2019
ISBN: 0236-1493
УДК: 004.032.26
DOI: 10.25018/0236-1493-2019-01-0-105-111
Authors: Kon'shin B. F., Yuskov V. S.

Authors' Information:
Kon'shin B.F., Candidate of Technical Sciences,
Assistant Professor, e-mail: mineur@bk.ru,
Yuskov V.S., Student, e-mail: v.yuskov@gmail.com,
National University of Science and Technology «MISiS»,
119049, Moscow, Russia.

Key words:
Ecology, prediction, ecological risks, neural networks, environmental standards, harmful substances, efficiency.

References:

1. Chizhova V. I., Plugotarenko N. K., Sosov P. A. Sistemnyy analiz i upravlenie riskami dlya zdorov'ya cheloveka na osnovanii dannykh avtomatizirovannoy sistemy monitoringa [System analysis and risk management for human health on the basis of automated monitoring system data], Inzhenernyy vestnik Dona. 2012, no 4, pp. 1—4. [In Russ].


2. Savitskaya T. V., Dudarov S. P., Egorov A. F., Levushkin A. S. Ispol'zovanie iskusstvennykh neyronnykh setey dlya prognozirovaniya zagryazneniya atmosfernogo vozdukha avariynymi istochnikami pri izmenyayushchikhsya meteousloviyakh [The use of artificial neural networks to predict air pollution by emergency sources under changing weather conditions], Ekologicheskie sistemy i pribory. 2007, no 10, pp. 67—72. [In Russ].


3. Stepanovskikh A. S. Ekologiya. Uchebnik dlya vuzov [Ecology. Textbook for high schools], Moscow, YUNITI-DANA, 2001, 703 p.


4. Gredel T. E., Allenbi B. R. Promyshlennaya ekologiya [Industrial ecology], Moscow, Izd-vo YUNITI, 2004, 430 p.


5. Denisov V. V., Kurbatova A. S., Denisova I. A., Bondarenko V. L., Grachev V. A., Gutenev V. A., Nagnibeda B. A. Ekologiya goroda [City ecology], Moscow, IKTS «Mart», 2008, 832 p.


6. Ivanov N., Fadin I. Inzhenernaya ekologiya i ekologicheskiy menedzhment [Engineering ecology and environmental management], Moscow, Logos, 2003, 528 p.


7. Mazurkin P. Statisticheskaya ekologiya: Uchebnoe posobie [Statistical ecology: Educational aid], Yoshkar-Ola, MarGU, 2004, 308 p.


8. Tikhomirova N. P. Metody analiza i upravleniya ekologo-ekonomicheskimi riskami: Uchebnoe posobie [Methods of analysis and management of environmental and economic risks: Educational aid], Moscow, Izd-vo YUNITI, 2003, 350 p.


9. Khotuntsev Yu. Ekologiya i ekologicheskaya bezopasnost': Uchebnoe posobie [Ecology and environmental safety: Educational aid], Moscow, Akademiya, 2004, 480 p.


10. Kruglov V. V., Borisov V. V. Iskusstvennye neyronnye seti. Teoriya i praktika, izd. 2-e [Artificial neural network. Theory and practice, 2nd edition], Moscow, Goryachaya liniya—Telekom, 2002, 382 p.


11. Tarkhov D. A. Neyronnye seti. Modeli i algoritmy [Neural network. Models and algorithms], Moscow, Kniga 18. Radiotekhnika, 2005, 256 p.


12. Kulikov E. I. Prikladnoy statisticheskiy analiz. 2-e izd. [Applied statistical analysis, 2nd edition], Moscow, GLT, 2008, 464 p.


Back
Site map