A Novel Smart Growth Evaluation Model by Applying RBFNN

DOI

10.2991/ammee-17.2017.27How to use a DOI?

Keywords

Smart Growth, AHP Model, BackÿPropagation Neural Network, RBF Neural Network

Abstract

In order to make cities can carry more population, save more resources and maintain rapid development, smart growth based on the three E's of sustainability and the 10 principles comes into being and is considered by many governments. In this paper, we concentrate on measure the success of smart growth and make plans. Firstly, we chose Arlington County and Canberra to research, then select 12 kinds of factors such as green space ratio preliminarily, which can reflect all aspects of the city. After that, we build the Entropy Method and chose the factors whose weight are highest 6. Next, we formulate the AHP Model to get the remained 6 factor's weight, and can also gain the approximate value of the metric. Then we build the BP Model to develop an accurate formula, but it traps into local extremum many times. Therefore, we use the more optimal model--the RBF Model, whose input is the above 6 factors and output is the value of the metric. To rank the 6 factors, we analyze the sensitivity of our models, and formulate the most suitable plan for cities' development based on the ranks, cities' unique characteristics including geographical conditions, local government expenditure, etc. At last, we proposed a novel method to evaluate a city fits in smart growth or not.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Jiaxing Zhang
PY  - 2017/06
DA  - 2017/06
TI  - A Novel Smart Growth Evaluation Model by Applying RBFNN
BT  - Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
PB  - Atlantis Press
SP  - 133
EP  - 136
SN  - 2352-5401
UR  - https://doi.org/10.2991/ammee-17.2017.27
DO  - 10.2991/ammee-17.2017.27
ID  - Zhang2017/06
ER  -