Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

Evaluation of Ability to Supply Water Based on GRNN Neural Network Model: Case of Beijing

Authors
Jiaming Tian
Corresponding Author
Jiaming Tian
Available Online March 2018.
DOI
https://doi.org/10.2991/mecae-18.2018.28How to use a DOI?
Keywords
Evaluating system; Ability to supply water; AHP; GRNN neural network.
Abstract
Water supply capacity is an important index for evaluating a city. In this paper, AHP (Analytic Hierarchy Process) is used to establish an index system and evaluation standard of the ability to supply water, and 15 evaluation indicators are put forward from 4 aspects of the relationship of supply and demand, the geographical factors, the economic factors and the environmental factors. Evaluation model for ability to supply water is established by the principle of GRNN (General Regression Neural Network) theory and methods. And then the model is used to comprehensively evaluate the water supply capacity of Beijing in 2005-2015, which is proved that the established GRNN evaluation model and evaluation method are reasonable and feasible.
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Proceedings
2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
Part of series
Advances in Engineering Research
Publication Date
March 2018
ISBN
978-94-6252-493-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/mecae-18.2018.28How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jiaming Tian
PY  - 2018/03
DA  - 2018/03
TI  - Evaluation of Ability to Supply Water Based on GRNN Neural Network Model: Case of Beijing
BT  - 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-18.2018.28
DO  - https://doi.org/10.2991/mecae-18.2018.28
ID  - Tian2018/03
ER  -