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- W2102019013 abstract "In recent years, extreme weather event due to the climate change has been frequently occurred over the world. Meanwhile, Korean peninsula has been suffered from the natural disasters such as snowfall. This study estimated the snowfall depth of climate change by using temperature, precipitation based on KMA-RegCM3 climate model and climate change scenario. We estimated the frequency based daily snowfall depth(50yr, 80yr, 100yr and 200yr) at 18 weather stations for four different target periods(Target I: 1971~2010, Target II: 2011~2040, Target III: 2041~2070, Target IV: 2071~2100) under climate change. Snowfall has nonlinear relationship with temperature and precipitation and so this study used a neural network and multiple regression models which can consider nonlinearity between snowfall and meteorological variables for its forecasting. As the results, the average rate of frequency based snowfall depth will be decreased by 6~18% for Target I and the rate will be continuously decreased in Target II, III and IV. The results of this study could be used as the basic information for the future disaster prevention planning and design criteria related to snowfall." @default.
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- W2102019013 date "2014-02-28" @default.
- W2102019013 modified "2023-09-27" @default.
- W2102019013 title "Estimation of Frequency Based Snowfall Depth Considering Climate Change Using Neural Network" @default.
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- W2102019013 doi "https://doi.org/10.9798/kosham.2014.14.1.93" @default.
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