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http://dx.doi.org/10.9720/kseg.2021.1.031

The Study for Improvement of Data-Quality of Cut-Slope Management System Using Machine Learning  

Lee, Se-Hyeok (Korea Institute of Civil Engineering and Building Technology)
Kim, Seung-Hyun (Korea Institute of Civil Engineering and Building Technology)
Woo, Yonghoon (Korea Institute of Civil Engineering and Building Technology)
Moon, Jae-Pil (Korea Institute of Civil Engineering and Building Technology)
Yang, Inchul (Korea Institute of Civil Engineering and Building Technology)
Publication Information
The Journal of Engineering Geology / v.31, no.1, 2021 , pp. 31-42 More about this Journal
Abstract
Database of Cut-slope management system (CSMS) has been constructed based on investigations of all slopes on the roads of the whole country. The investigation data is documented by human, so it is inevitable to avoid human-error such as missing-data and incorrect entering data into computer. The goal of this paper is constructing a prediction model based on several machine-learning algorithms to solve those imperfection problems of the CSMS data. First of all, the character-type data in CSMS data must be transformed to numeric data. After then, two algorithms, i.g., multinomial logistic regression and deep-neural-network (DNN), are performed, and those prediction models from two algorithms are compared. Finally, it is identified that the accuracy of DNN-model is better than logistic model, and the DNN-model will be utilized to improve data-quality.
Keywords
cut-slope management system (CSMS); missing-data; incorrect input; machine-learning; multinomial logistic regression; deep-neural-network;
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