Application of Classifiers in Predicting Problems of Hydropower Engineering

Liming Huang; Yi Chen; Chunyong She; Yangfeng Wu; Shuai Zhang

doi:doi:10.11648/j.acm.20180703.19

| Peer-Reviewed

Application of Classifiers in Predicting Problems of Hydropower Engineering

Liming Huang, Yi Chen, Chunyong She, Yangfeng Wu, Shuai Zhang

Published in Applied and Computational Mathematics (Volume 7, Issue 3)

Received: 18 July 2018 Published: 19 July 2018

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

It’s of vital importance to supervise hydropower engineering in order to make better use of water resources. To supervise it efficiently and effectively, it’s advisable to predict potential problems of hydropower engineering beforehand, after which the people concerned can inspect problems accordingly. Due to the complexity and large quantity of data, data mining techniques are indispensable and useful when making predictions. This study compares performance of Random Forest, C4.5 and Naïve Bayes on the basis of accuracy, precision, recall and F-measure. It comes out that Random Forest is more suitable for this problem. For purpose of more precise results, numbers of trees and features are determined in advance before constructing the forest. Furthermore, which feature influences the prediction result most is also investigated.

Published in	Applied and Computational Mathematics (Volume 7, Issue 3)
DOI	10.11648/j.acm.20180703.19
Page(s)	139-145
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Data Mining, Prediction, Classification Models, Hydropower Engineering Supervision

References

[1]	Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. American Statistician, 46 (3), 175-185.
[2]	Breiman, L., Friedman, J., Olshcn, R. A., et al. (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software. ISBN 978-0-412-04841-8.
[3]	Deng, W., Wang, G., & Zhang, X. (2015). A novel hybrid water quality time series prediction method based on cloud model and fuzzy forecasting. Chemometrics and Intelligent Laboratory Systems, 149, 39-49.
[4]	Deng, W., & Wang, G. (2017). A novel water quality data analysis framework based on time-series data mining. Journal of Environmental Management, 196, 365-375.
[5]	Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (8), 832-844.
[6]	Lu, S., Wang, J., & Xue, Y. (2016). Study on multi-fractal fault diagnosis based on emd fusion in hydraulic engineering. Applied Thermal Engineering, 103, 798-806.
[7]	Su, H., Li, X., Yang, B., & Wen, Z. (2018). Wavelet support vector machine-based prediction model of dam deformation. Mechanical Systems and Signal Processing, 110, 412-427.
[8]	Stehman, S. V. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62 (1), 77-89.
[9]	Yin, Y., & Shang, P. (2016). Forecasting traffic time series with multivariate predicting method. Applied Mathematics and Computation, 291, 266-278.
[10]	Zhang, H., Kang, Y., Zhu, Y., et al. (2017). Novel naïve bayes classification models for predicting the chemical ames mutagenicity. Toxicology in Vitro, 41, 56-62.

Cite This Article

Plain Text BibTeX RIS

APA Style

Liming Huang, Yi Chen, Chunyong She, Yangfeng Wu, Shuai Zhang. (2018). Application of Classifiers in Predicting Problems of Hydropower Engineering. Applied and Computational Mathematics, 7(3), 139-145. https://doi.org/10.11648/j.acm.20180703.19

Copy | Download

ACS Style

Liming Huang; Yi Chen; Chunyong She; Yangfeng Wu; Shuai Zhang. Application of Classifiers in Predicting Problems of Hydropower Engineering. Appl. Comput. Math. 2018, 7(3), 139-145. doi: 10.11648/j.acm.20180703.19

Copy | Download

AMA Style

Liming Huang, Yi Chen, Chunyong She, Yangfeng Wu, Shuai Zhang. Application of Classifiers in Predicting Problems of Hydropower Engineering. Appl Comput Math. 2018;7(3):139-145. doi: 10.11648/j.acm.20180703.19

Copy | Download

@article{10.11648/j.acm.20180703.19,
  author = {Liming Huang and Yi Chen and Chunyong She and Yangfeng Wu and Shuai Zhang},
  title = {Application of Classifiers in Predicting Problems of Hydropower Engineering},
  journal = {Applied and Computational Mathematics},
  volume = {7},
  number = {3},
  pages = {139-145},
  doi = {10.11648/j.acm.20180703.19},
  url = {https://doi.org/10.11648/j.acm.20180703.19},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20180703.19},
  abstract = {It’s of vital importance to supervise hydropower engineering in order to make better use of water resources. To supervise it efficiently and effectively, it’s advisable to predict potential problems of hydropower engineering beforehand, after which the people concerned can inspect problems accordingly. Due to the complexity and large quantity of data, data mining techniques are indispensable and useful when making predictions. This study compares performance of Random Forest, C4.5 and Naïve Bayes on the basis of accuracy, precision, recall and F-measure. It comes out that Random Forest is more suitable for this problem. For purpose of more precise results, numbers of trees and features are determined in advance before constructing the forest. Furthermore, which feature influences the prediction result most is also investigated.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Application of Classifiers in Predicting Problems of Hydropower Engineering
AU  - Liming Huang
AU  - Yi Chen
AU  - Chunyong She
AU  - Yangfeng Wu
AU  - Shuai Zhang
Y1  - 2018/07/19
PY  - 2018
N1  - https://doi.org/10.11648/j.acm.20180703.19
DO  - 10.11648/j.acm.20180703.19
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 139
EP  - 145
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20180703.19
AB  - It’s of vital importance to supervise hydropower engineering in order to make better use of water resources. To supervise it efficiently and effectively, it’s advisable to predict potential problems of hydropower engineering beforehand, after which the people concerned can inspect problems accordingly. Due to the complexity and large quantity of data, data mining techniques are indispensable and useful when making predictions. This study compares performance of Random Forest, C4.5 and Naïve Bayes on the basis of accuracy, precision, recall and F-measure. It comes out that Random Forest is more suitable for this problem. For purpose of more precise results, numbers of trees and features are determined in advance before constructing the forest. Furthermore, which feature influences the prediction result most is also investigated.
VL  - 7
IS  - 3
ER  -

Copy | Download

Author Information

Liming Huang

Quality and Safety Inspection Center of Hydropower Engineering of Zhejiang Province, Hangzhou, China
Yi Chen

School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Chunyong She

Quality and Safety Inspection Center of Hydropower Engineering of Zhejiang Province, Hangzhou, China
Yangfeng Wu

Quality and Safety Inspection Center of Hydropower Engineering of Zhejiang Province, Hangzhou, China
Shuai Zhang

School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Liming Huang, Yi Chen, Chunyong She, Yangfeng Wu, Shuai Zhang. (2018). Application of Classifiers in Predicting Problems of Hydropower Engineering. Applied and Computational Mathematics, 7(3), 139-145. https://doi.org/10.11648/j.acm.20180703.19

Copy | Download

ACS Style

Liming Huang; Yi Chen; Chunyong She; Yangfeng Wu; Shuai Zhang. Application of Classifiers in Predicting Problems of Hydropower Engineering. Appl. Comput. Math. 2018, 7(3), 139-145. doi: 10.11648/j.acm.20180703.19

Copy | Download

AMA Style

Liming Huang, Yi Chen, Chunyong She, Yangfeng Wu, Shuai Zhang. Application of Classifiers in Predicting Problems of Hydropower Engineering. Appl Comput Math. 2018;7(3):139-145. doi: 10.11648/j.acm.20180703.19

Copy | Download

@article{10.11648/j.acm.20180703.19,
  author = {Liming Huang and Yi Chen and Chunyong She and Yangfeng Wu and Shuai Zhang},
  title = {Application of Classifiers in Predicting Problems of Hydropower Engineering},
  journal = {Applied and Computational Mathematics},
  volume = {7},
  number = {3},
  pages = {139-145},
  doi = {10.11648/j.acm.20180703.19},
  url = {https://doi.org/10.11648/j.acm.20180703.19},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20180703.19},
  abstract = {It’s of vital importance to supervise hydropower engineering in order to make better use of water resources. To supervise it efficiently and effectively, it’s advisable to predict potential problems of hydropower engineering beforehand, after which the people concerned can inspect problems accordingly. Due to the complexity and large quantity of data, data mining techniques are indispensable and useful when making predictions. This study compares performance of Random Forest, C4.5 and Naïve Bayes on the basis of accuracy, precision, recall and F-measure. It comes out that Random Forest is more suitable for this problem. For purpose of more precise results, numbers of trees and features are determined in advance before constructing the forest. Furthermore, which feature influences the prediction result most is also investigated.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Application of Classifiers in Predicting Problems of Hydropower Engineering
AU  - Liming Huang
AU  - Yi Chen
AU  - Chunyong She
AU  - Yangfeng Wu
AU  - Shuai Zhang
Y1  - 2018/07/19
PY  - 2018
N1  - https://doi.org/10.11648/j.acm.20180703.19
DO  - 10.11648/j.acm.20180703.19
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 139
EP  - 145
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20180703.19
AB  - It’s of vital importance to supervise hydropower engineering in order to make better use of water resources. To supervise it efficiently and effectively, it’s advisable to predict potential problems of hydropower engineering beforehand, after which the people concerned can inspect problems accordingly. Due to the complexity and large quantity of data, data mining techniques are indispensable and useful when making predictions. This study compares performance of Random Forest, C4.5 and Naïve Bayes on the basis of accuracy, precision, recall and F-measure. It comes out that Random Forest is more suitable for this problem. For purpose of more precise results, numbers of trees and features are determined in advance before constructing the forest. Furthermore, which feature influences the prediction result most is also investigated.
VL  - 7
IS  - 3
ER  -

Copy | Download