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http://dx.doi.org/10.15207/JKCS.2019.10.10.007

OrdinalEncoder based DNN for Natural Gas Leak Prediction  

Khongorzul, Dashdondov (Dept Computer Engineering, Chungbuk National University)
Lee, Sang-Mu (Dept Computer Engineering, Chungbuk National University)
Kim, Mi-Hye (Dept Computer Engineering, Chungbuk National University)
Publication Information
Journal of the Korea Convergence Society / v.10, no.10, 2019 , pp. 7-13 More about this Journal
Abstract
The natural gas (NG), mostly methane leaks into the air, it is a big problem for the climate. detected NG leaks under U.S. city streets and collected data. In this paper, we introduced a Deep Neural Network (DNN) classification of prediction for a level of NS leak. The proposed method is OrdinalEncoder(OE) based K-means clustering and Multilayer Perceptron(MLP) for predicting NG leak. The 15 features are the input neurons and the using backpropagation. In this paper, we propose the OE method for labeling target data using k-means clustering and compared normalization methods performance for NG leak prediction. There five normalization methods used. We have shown that our proposed OE based MLP method is accuracy 97.7%, F1-score 96.4%, which is relatively higher than the other methods. The system has implemented SPSS and Python, including its performance, is tested on real open data.
Keywords
Natural Gas; OrdinalEncoder; MLP; K-means; F1-score;
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Times Cited By KSCI : 2  (Citation Analysis)
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