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http://dx.doi.org/10.17663/JWR.2018.20.2.105

Frequency Analysis Using Bootstrap Method and SIR Algorithm for Prevention of Natural Disasters  

Kim, Yonsoo (Risk Management Institute, LIG System Co., Ltd.)
Kim, Taegyun (Landscape Architecture, Gyeongnam National University of science and Technology)
Kim, Hung Soo (Department of Civil Engineering, Inha University)
Noh, Huisung (Hydro Science and Engineering Research Institute, KICT)
Jang, Daewon (Risk Management Institute, LIG System Co., Ltd.)
Publication Information
Journal of Wetlands Research / v.20, no.2, 2018 , pp. 105-115 More about this Journal
Abstract
The frequency analysis of hydrometeorological data is one of the most important factors in response to natural disaster damage, and design standards for a disaster prevention facilities. In case of frequency analysis of hydrometeorological data, it assumes that observation data have statistical stationarity, and a parametric method considering the parameter of probability distribution is applied. For a parametric method, it is necessary to sufficiently collect reliable data; however, snowfall observations are needed to compensate for insufficient data in Korea, because of reducing the number of days for snowfall observations and mean maximum daily snowfall depth due to climate change. In this study, we conducted the frequency analysis for snowfall using the Bootstrap method and SIR algorithm which are the resampling methods that can overcome the problems of insufficient data. For the 58 meteorological stations distributed evenly in Korea, the probability of snowfall depth was estimated by non-parametric frequency analysis using the maximum daily snowfall depth data. The results of frequency based snowfall depth show that most stations representing the rate of change were found to be consistent in both parametric and non-parametric frequency analysis. According to the results, observed data and Bootstrap method showed a difference of -19.2% to 3.9%, and the Bootstrap method and SIR(Sampling Importance Resampling) algorithm showed a difference of -7.7 to 137.8%. This study shows that the resampling methods can do the frequency analysis of the snowfall depth that has insufficient observed samples, which can be applied to interpretation of other natural disasters such as summer typhoons with seasonal characteristics.
Keywords
Bootstrap Method; SIR Algorithm; Snowfall Depth; Frequency Analysis;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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