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Application of Classifiers in Predicting Problems of Hydropower Engineering

Received: 18 July 2018     Published: 19 July 2018
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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.
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  • 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

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    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

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    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

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  • @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}
    }
    

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  • 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  - 

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Author Information
  • Quality and Safety Inspection Center of Hydropower Engineering of Zhejiang Province, Hangzhou, China

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

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

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

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

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