Browse > Article
http://dx.doi.org/10.17663/JWR.2019.21.2.157

Categorical Prediction and Improvement Plan of Snow Damage Estimation using Random Forest  

Lee, Hyeong Joo (Department of Civil Engineering, Hoseo university)
Chung, Gunhui (Department of Civil Engineering, Hoseo university)
Publication Information
Journal of Wetlands Research / v.21, no.2, 2019 , pp. 157-162 More about this Journal
Abstract
Recently, the occurrence of unusual heavy snow and cold are increasing due to the unusual global climate change. In particular, the temperature dropped to minus 69 degrees Celsius in the United States on January 8, 2018. In Korea, on February 17, 2014, the auditorium building in Gyeongju Mauna Resort was collapsed due to the heavy snowfall. Because of the tragic accident many studies on the reduction of snow damage is being conducted, but it is difficult to predict the exact damage due to the lack of historical damage data, and uncertainty of meteorological data due to the long distance between the damaged area and the observatory. Therefore, in this study, available data were collected from factors that are thought to be corresponding to snow damage, and the amount of snow damage was estimated categorically using a random forest. At present, the prediction accuracy was not sufficient due to lack of historical damage data and changes of the design code for green houses. However, if accurate weather data are obtained in the affected areas. the accuracy of estimates would increase enough for being used for be the degree preparedness of disaster management.
Keywords
snow damage; estimation of damage; random forest;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Kwon, SH, Park, HS, Chung, GH, (2016). Analysis of snow vulnerability and adaptation policy for heavy snow, J. of Korean Society of Hazard Mitigation, 16(2), pp. 363-368. [Korean Literature]. DOI : https://doi.org/10.9798/KOSHAM.2016.16.2.363   DOI
2 Kwon, SH, Chung, GH, (2017). Estimation of snow damages using multiple regression model: The Case of Gangwon Province, J. of the Korean Society of Civil Engineers, 37(1), pp. 61-72. [Korean Literature]. DOI: https://doi.org/10.9798/KOSHAM.2016.16.2.437   DOI
3 MPSS(Ministry of Public Safety and Security)(2014). The 2013 Annual Natural Disaster report, Ministry of Public Safety and Security. [Korean Literature]
4 Oh, YR, Chung, GH, (2017). Estimation of snow damage and proposal of snow damage threshold based on historical disaster data, J. of the Korean Society of Civil Engineers, 37(2), pp. 325-331. [Korean Literature]. DOI : https://doi.org/10.12652/ksce.2017.37.2.0325   DOI
5 Oh, YR, Chung, GH, (2018). Multiple regression models of snow damage prediction according to the snow damage vulnerability groups Korea, J. of Korean Society of Hazard Mitigation, 18(2), pp. 355-359. [Korean Literature]. DOI: https://doi.org/10.9798/KOSHAM.2018.18.2.355   DOI
6 Shannon, CE, Weaver, W, (1949). The mathematical theory of communication, The University of Illinois Press, Urbana, U.S.A
7 Breiman, L, (2001). Random forests machine learning, 45(1), pp. 5-32. DOI : https://doi.org/10.1023/A:1010933404324   DOI
8 Choi, CH, Park, KH, Park, HK, Lee, MG, Kim, JS, Kim, HS, (2017). Development of heavy rain damage prediction function for public facility using machine learning, J. of Korean Society of Hazard Mitigation, 17(6), pp. 443-450. [Korean Literature]. DOI : https://doi.org/10.9798/KOSHAM.2017.17.6.443   DOI
9 Ha, R, Shin, HJ, Kim, SJ, (2007). Proposal of prediction technique for future vegetation information by climate change using satellite image, J. of Korean Association of Geographic Information Studies, 10(3), pp. 58-69. [Korean Literature] DOI : http://www.koreascience.or.kr/article/JAKO200721761942397.page