Articles | Volume 22, issue 10
https://doi.org/10.5194/nhess-22-3435-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-3435-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development of black ice prediction model using GIS-based multi-sensor model validation
Seok Bum Hong
Interdisciplinary Program for Crisis, Disaster and Risk Management,
Sungkyunkwan University, Suwon, 16419, Korea
Hong Sik Yun
Interdisciplinary Program for Crisis, Disaster and Risk Management,
Sungkyunkwan University, Suwon, 16419, Korea
School of Civil, Architectural Engineering and Landscape
Architecture, Sungkyunkwan University, Suwon, 16419, Korea
Sang Guk Yum
Department of Civil Engineering, College of Engineering,
Gangneung-Wonju National University, Gangneung, 25457, Korea
Seung Yeop Ryu
Disaster Management Office, Korea Expressway Corporation, Gimcheon,
39660, Korea
In Seong Jeong
Disaster and Safety Inspection Division, Disaster Management
Cooperation Office, Ministry of the Interior and Safety, Sejong, 30116, Korea
School of Civil, Architectural Engineering and Landscape
Architecture, Sungkyunkwan University, Suwon, 16419, Korea
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In this study, emerging engineering techniques such as machine learning and deep learning technique was applied to predict heavy snowfall prediction in the Korean Peninsula. More specifically, it was observed that the predictive model using the RFR algorithm had the best performance based on a comparison between the observed and predicted data. In addition, it was observed that the performance of the ensemble models (RFR and XGB) was better than that of the single regression models.
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Short summary
This study advances previous models through machine learning and multi-sensor-verified results. Using spatial and meteorological data from the study area (Suncheon–Wanju Highway in Gurye-gun), the amount and location of black ice were modelled based on system dynamics to predict black ice and then simulated with the geographic information system (m2). Based on the model results, multiple sensors were buried at four selected points in the study area, and the model was compared with sensor data.
This study advances previous models through machine learning and multi-sensor-verified results....
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