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.
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