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

• Published in: Natural hazards and earth system sciences 2022-10, Vol.22 (10), p.3435-3459
• Main Authors: Hong, Seok Bum, Yun, Hong Sik, Yum, Sang Guk, Ryu, Seung Yeop, Jeong, In Seong, Kim, Jisung
• Format: Article
• Language: English
• Subjects: Accidents; Air temperature; Algorithms; Artificial neural networks; Bridges; Cloud cover; Cloudiness; Fast Fourier transformations; Fatalities; Fourier transforms; Freezing; Freezing rain; Geographic information systems; Geographical information systems; Government agencies; Heat; Highways; Humidity; Ice; Ice formation; Icing; Information systems; Latent heat; Machine learning; Mathematical models; Modelling; Moisture effects; Neural networks; Numerical methods; Precipitation; Precipitation (Meteorology); Prediction models; Radiation; Rain; Rainfall; Regression analysis; Regression models; Remote sensing; Roads; Roads & highways; Sensors; Simulation; Snow; Snow cover; Solar energy; Solar radiation; Spatial data; Specific heat; System dynamics; Traffic; Traffic accidents; Traffic accidents & safety; Vapor pressure; Vapour pressure; Wind speed
• ISSN: 1684-9981; 1561-8633; 1684-9981
• DOI: 10.5194/nhess-22-3435-2022

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, river system, bridge, and 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 a geographic information system in units of square metres. 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 frequency = 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.