• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.2
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• pp.325-331
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• 2017

Due to the climate change, natural disaster has been occurred more frequently and the number of snow disasters has been also increased. Therefore, many researches have been conducted to predict the amount of snow damages and to reduce snow damages. In this study, snow damages over last 21 years on the Natural Disaster Report were analyzed. As a result, Chungcheong-do, Jeolla-do, and Gangwon-do have the highest number of snow disasters. The multiple linear regression models were developed using the snow damage data of these three provinces. Daily fresh snow depth, daily maximum, minimum, and average temperatures, and relative humidity were considered as possible inputs for climate factors. Inputs for socio-economic factors were regional area, greenhouse area, farming population, and farming population over 60. Different regression models were developed based on the daily maximum snow depth. As results, the model efficiency considering all damage (including low snow depth) data was very low, however, the model only using the high snow depth (more than 25 cm) has more than 70% of fitness. It is because that, when the snow depth is high, the snow damage is mostly caused by the snow load itself. It is suggested that the 25 cm of snow depth could be used as the snow damage threshold based on this analysis.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.4
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• pp.98-106
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• 2003

In order to set up structural improvement strategy against meteorological disasters of the shading structures in ginseng fields, structural safety analyses as well as some case studies of structural damage patterns were carried out. According to the results of structural safety analysis, allowable safe snow depth for type B(wood frame with single span) was 25.9 cm, and those for type A(wood frame with multi span) and type C and D (steel frame with multi span) were 17.6 cm, 25.8 cm, and 20.0 cm respectively. So types of shading structures should be selected according to the regional design snow depth. An experiential example study on meteorological disasters indicated that a strong wind damage was experienced once every 20 years, and a heavy snow damage once every 9.5 years. The most serious disasters were caused by heavy snow and it was found that a half break and complete collapse of structures were experienced by about 70% of snow damage. In addition to maintenance, repair and reinforcement, it is also recommended that improved model of shading structures for ginseng cultivation should be developed as a long term countermeasures against meteorological disasters.

• Journal of Wetlands Research
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• v.21 no.2
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• pp.157-162
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• 2019

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.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.2
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• pp.75-84
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• 2021

As the weather changes become frequent, weather disasters are increasing, causing more damage to plastic greenhouses. Among the damage caused by various disasters, damage by snow to the greenhouse takes a relatively long time, so if an alarm system is properly prepared, the damage can be reduced. Existing greenhouse design standards and snow warning systems are based on snow depth. However, even in the same depth, the load on the greenhouse varies depending on meteorological characteristics and snow density. Therefore, this study aims to secure the structural safety of greenhouses by developing sensors that can directly measure snow loads, and analysing the warning criteria for load using a stochastic model. Markov chain was applied to estimate the failure probability of various types of greenhouses in various regions, which let users actively cope with heavy snowfall by selecting an appropriate time to respond. Although it was hard to predict the precise snow depth or amounts, it could successfully assess the risk of structures by directly detecting the snow load using the developed sensor.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.345-352
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• 2023

Recently, the risk of snow disasters has been increasing South Korea. The damages of heavy snow were categorized into direct and indirect. Direct damage is usually the collapse of buildings as houses, greenhouse or barns. Indirect damage is various, for example, traffic congestion, traffic acident, drop damage, and so on. In South Korea, direct damage is severe in rural area, mosty collapse of greenhouse or barns. However, indirect damage such as traffic accident is mostly occurred in urban area. Therefore, the regional characteristics should be considered when vulnerability is evaluated. Therefore, in this study, the PSR and DPSIR method were applied by regional scale in South Korea. The PSR evaluation method is divided into pressure, state, and reaction index. however, the DPSIR evaluation method is divided into Driving force, Pressure, State, Impact, and Response index. the DPSIR evaluation method is divided into Driving force, Pressure, State, Impact, and Response index. Data corresponding to each indicator were collected, and the weight was calculated using the entropy method to calculate the snowfall vulnerability index by regional scale in South Korea. Calculated heavy snow damage vulnerabilities from the two methods were compared. The calculated vulnerabilities were validated using the recent snow damage in South Korea from 2018 to 2022. Snow vulnerability index calculated using the DPSIR method showed more reliable results. The results of this study could be utilized as an information to prepare the mitigation of heavy snow damage and to establish an efficient snow removal response system.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.77-93
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• 2022

In this study, we present an algorithm that analyzes the risk by reflecting regional characteristics for factors affected by direct and indirect damage from heavy-snow. Factors affected by heavy-snow damage by 29 regions are selected as influencing variables, and the concept of sensitivity is derived through the relationship with the amount of damage. A snow damage risk prediction model was developed using a machine learning (XGBoost) algorithm by setting weather conditions (snow cover, humidity, temperature) and sensitivity as independent variables, and setting the risk derived according to changes in the independent variables as dependent variables.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.4
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• pp.107-113
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• 2002

The pipe framed and arch shape plastic greenhouse, which is the most popular greenhouse in Korea, is relatively weak in snowdrift. Reinforcement of rigid frame or column is required to reduce the damage from heavy snow in this type. But additional rigid frames or columns decrease light transmissivity or workability, and increase construction cost. So it is desirable to prepare some temporary poles and to install them when the warning of heavy snow is announced. This study was carried out to develop the temporary pole supporting system using galvanized steel pipes for plastic housing and to evaluate the safe snow load on a temporary pole. A pipe connector, which is inserted in the top of pipe used in the temporary pole and supports the center purline, was designed and manufactured to be able to carry the upper loads safely. And a bearing plate was safely designed and manufactured in order to carry the loads acting on it to the ground. When temporary poles of ${\phi}$ 25 pipe are installed at 2.4m interval, it shows that the single span plastic greenhouses with 5~7 m width are able to support the additional snow depth of 13.9~25.3 cm beyond the snow load supported by main frame.

• Journal of Forest and Environmental Science
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• v.24 no.3
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• pp.137-142
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• 2008

In this paper, we apply a logistic regression model to the data of snow damage on sugi (Cryptomeria japonica) occurred in Toyama prefecture (in Japan) in 2004 for estimating the risk probability. In order to specify the factors effecting snow damage, we apply a model selection procedure determining optimal subset of explanatory variables. In this process we consider the following 3 information criteria, 1) Akaike's information criterion, 2) Baysian information criterion, 3) Bias-corrected Akaike's information criterion. For the selected variables, we give a proper interpretation from the viewpoint of natural disaster.

• Journal of Korea Planning Association
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• v.53 no.6
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• pp.117-130
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• 2018

Heavy snow is a natural disaster that causes serious economic damage. Since snowfall has been increasing recently, there is a need for measures against heavy snowfall. In order to make a policy decision on heavy snowfall, it is necessary to estimate the precise amount of damage by heavy snowfall. The direct damage of the heavy snow is severe, however the indirect damage caused by the road congestion and the urban dysfunction is also serious. Therefore, it is necessary to estimate indirect damage of snowfall. The purpose of this study is to estimate the effects on the regional economy from the limitation in traffic logistics caused by heavy snow using the transport demand model and inoperability input-output Model. The result shows that the amount of production loss caused by the heavy snow is KRW 2,460 billion per year and if the period of snowfall removal is shortened by one day or two days, it could be reduced to KRW 1,219 or 2,787 billion in production loss.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1125-1135
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• 2022

Damages by heavy snow does not occur very often, but when it does, it causes damage to a wide area. To mitigate snow damage, it is necessary to know, in advance, the depth of snow that causes damage in each region. However, snow depths are measured at observatory locations, and it is difficult to understand the spatial distribution of snow depth that causes damage in a region. To understand the spatial distribution of snow depth, the point measurements are interpolated. However, estimating spatial distribution of snow depth is not easy when the number of measured snow depth is small and topographical characteristics such as altitude are not similar. To overcome this limit, satellite images such as Synthetic Aperture Radar (SAR) can be analyzed using Differential Interferometric SAR (DInSAR) method. DInSAR uses two different SAR images measured at two different times, and is generally used to track minor changes in topography. In this study, the spatial distribution of snow depth was estimated by DInSAR analysis using dual polarimetric IW mode C-band SAR data of Sentinel-1B satellite operated by the European Space Agency (ESA). In addition, snow depth was estimated using geostationary satellite Chollian-2 (GK-2A) to compare with the snow depth from DInSAR method. As a result, the accuracy of snow cover estimation in terms with grids was about 0.92% for DInSAR and about 0.71% for GK-2A, indicating high applicability of DInSAR method. Although there were cases of overestimation of the snow depth, sufficient information was provided for estimating the spatial distribution of the snow depth. And this will be helpful in understanding regional damage-causing snow depth.