Preprints
https://doi.org/10.5194/nhess-2022-147
https://doi.org/10.5194/nhess-2022-147
 
13 Jun 2022
13 Jun 2022
Status: a revised version of this preprint was accepted for the journal NHESS and is expected to appear here in due course.

Development of black ice prediction model using GIS-based multi-sensor model validation

Seok Bum Hong1, Hong Sik Yun1,2, Sang Guk Yum3, Seung Yeop Ryu4, In Seong Jeong5, and Jisung Kim3 Seok Bum Hong et al.
  • 1Interdisciplinary Program for Crisis, Disaster and Risk Management, Sungkyunkwan University, Suwon, 16419, Korea
  • 2School of Civil, Architectural Engineering & Landscape Architecture, Sungkyunkwan University, Suwon, 16419, Korea
  • 3Department of Civil Engineering, College of Engineering, Gangneung-Wonju National University, Gangneung, 25457, Korea
  • 4Disaster Management Office, Korea Expressway Corporation, Gimcheon, 39660, Korea
  • 5Disaster and Safety Inspection Division, Disaster Management Cooperation Office, Ministry of the Interior and safety, Sejong, 30116, Korea

Abstract. Fog, freezing rain and snow (melt) quickly condense on road surfaces, forming black ice that is difficult to identify and causes major accidents on highways. As a countermeasure to prevent icing car accidents, it is necessary to predict the amount and location of black ice. This study advanced previous models through machine learning and multi-sensor verified results. Using spatial (hill shade, slope, river system, bridge, and road (highway) and meteorological (air temperature, cloudiness, vapour pressure, wind speed, precipitation, snow cover, specific heat, latent heat, and solar radiation energy) data from the study area (Suncheon-Wanju Highway in Gurye-gun, Jeollanam-do, South Korea), the amount and location of black ice were modelled based on system dynamics to predict black ice and then simulated with Geo-Information System in units of 1 m2. The intermediate factors calculated as input factors were road temperature and road moisture, modelled using a deep neural network (DNN) and numerical methods. Considering the results of the DNN, the root mean square error was improved by 148.6 % and reliability by 11.43 % compared to a previous study (Linear Regression). Based on the model results, multiple sensors were buried at four selected points in the study area. The model was compared with sensor data and verified with the upper-tailed test (with a significance level of 0.05) and Fast Fourier Transform (freezing does not occur when wavelength = 0.00001 Hz). Results of the verified simulation can provide valuable data for government agencies like road traffic authorities to prevent traffic accidents caused by black ice.

Seok Bum Hong et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-147', Anonymous Referee #1, 10 Aug 2022
    • AC1: 'Reply on RC1', Seok Bum Hong, 06 Sep 2022
    • AC3: 'Reply on RC1', Seok Bum Hong, 10 Sep 2022
  • RC2: 'Comment on nhess-2022-147', Anonymous Referee #2, 23 Aug 2022
    • AC2: 'Reply on RC2', Seok Bum Hong, 06 Sep 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-147', Anonymous Referee #1, 10 Aug 2022
    • AC1: 'Reply on RC1', Seok Bum Hong, 06 Sep 2022
    • AC3: 'Reply on RC1', Seok Bum Hong, 10 Sep 2022
  • RC2: 'Comment on nhess-2022-147', Anonymous Referee #2, 23 Aug 2022
    • AC2: 'Reply on RC2', Seok Bum Hong, 06 Sep 2022

Seok Bum Hong et al.

Seok Bum Hong et al.

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Short summary
This study advanced 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 Geo-Information System in units of 1 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.
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