Application of Classifiers in Predicting Problems of Hydropower Engineering
Applied and Computational Mathematics
Volume 7, Issue 3, June 2018, Pages: 139-145
Received: Jul. 18, 2018;
Published: Jul. 19, 2018
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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
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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.
Data Mining, Prediction, Classification Models, Hydropower Engineering Supervision
To cite this article
Application of Classifiers in Predicting Problems of Hydropower Engineering, Applied and Computational Mathematics.
Vol. 7, No. 3,
2018, pp. 139-145.
Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. American Statistician, 46 (3), 175-185.
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.
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.
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.
Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (8), 832-844.
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.
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.
Stehman, S. V. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62 (1), 77-89.
Yin, Y., & Shang, P. (2016). Forecasting traffic time series with multivariate predicting method. Applied Mathematics and Computation, 291, 266-278.
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.