Proceedings of the 2016 International Conference on Education, Management and Computer Science

Improved Entropy Method Establishing Combination Model for Prediction Foundation Settlement

Authors
Yanping Gao, Yang Yang, Jie Wang
Corresponding Author
Yanping Gao
Available Online May 2016.
DOI
https://doi.org/10.2991/icemc-16.2016.175How to use a DOI?
Keywords
Improved entropy method; Exponential model; Logistic model; Combination model; Settlement prediction
Abstract
On the basis of the exponential model and Logistic model presented at the previous, using improved entropy method to calculate each model weight coefficient to establish a new combination model for prediction foundation settlement, to achieve the goal of comprehensive advantages of both models and smaller prediction error. Respectively using exponential model, Logistic model and a combination of both a model to process power plant foundation settlement data, and with squared error, mean absolute error, mean absolute percentage error, mean square error for each model to evaluate the prediction accuracy. The results show that Prediction error of combination model established by improved entropy method is less than a single forecast model. The combination model has Higher prediction accuracy and effectively predict the power plant foundation settlement.
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Conference on Education, Management and Computer Science
Series
Advances in Intelligent Systems Research
Publication Date
May 2016
ISBN
978-94-6252-202-2
ISSN
1951-6851
DOI
https://doi.org/10.2991/icemc-16.2016.175How 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  - Yanping Gao
AU  - Yang Yang
AU  - Jie Wang
PY  - 2016/05
DA  - 2016/05
TI  - Improved Entropy Method Establishing Combination Model for Prediction Foundation Settlement
BT  - Proceedings of the 2016 International Conference on Education, Management and Computer Science
PB  - Atlantis Press
SP  - 869
EP  - 876
SN  - 1951-6851
UR  - https://doi.org/10.2991/icemc-16.2016.175
DO  - https://doi.org/10.2991/icemc-16.2016.175
ID  - Gao2016/05
ER  -