Proceedings of the 2016 International Conference on Computational Science and Engineering (ICCSE 2016)

Study on Prediction Model of Tourist Amount of Scenic Spot Based on BP Neural Network Algorithm

Authors
Yongqiu Liu
Corresponding Author
Yongqiu Liu
Available Online October 2016.
DOI
https://doi.org/10.2991/iccse-16.2016.4How to use a DOI?
Keywords
Neural network algorithm, Mount Tai Scenic Spot, Tourist amount
Abstract

In recent years, the expansion of tourism market has put forward new requirements to strengthen the management of scenic spots, in this paper, it takes Mount Tai Scenic Area as the researching object, establishing the prediction model of tourist amount by using BP neural network algorithm, so as to predict the amount of tourists of scenic spots. Through the experimental result, we can know that the algorithm of this model is fast and effective. Therefore, it can verify the possibility of using neural network algorithm to predict tourist amount in the scenic spots.

Copyright
© 2016, 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/).

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Volume Title
Proceedings of the 2016 International Conference on Computational Science and Engineering (ICCSE 2016)
Series
Advances in Computer Science Research
Publication Date
October 2016
ISBN
978-94-6252-239-8
ISSN
2352-538X
DOI
https://doi.org/10.2991/iccse-16.2016.4How to use a DOI?
Copyright
© 2016, 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/).

Cite this article

TY  - CONF
AU  - Yongqiu Liu
PY  - 2016/10
DA  - 2016/10
TI  - Study on Prediction Model of Tourist Amount of Scenic Spot Based on BP Neural Network Algorithm
BT  - Proceedings of the 2016 International Conference on Computational Science and Engineering (ICCSE 2016)
PB  - Atlantis Press
SP  - 18
EP  - 21
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccse-16.2016.4
DO  - https://doi.org/10.2991/iccse-16.2016.4
ID  - Liu2016/10
ER  -