• Journal of the Korea Safety Management & Science
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• v.17 no.1
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• pp.169-177
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• 2015

We develop a model to predict typhoons in Korea. We collect data for typhoons and classify those depending on the severity level. Following a Bayesian approach, we develop a model that explains the relationship between different levels of typhoons. Through the analysis of the model, we can predict the rate of typhoons, the probability of approaching Korean peninsular, and the probability of striking Korean peninsular. We show that the uncertainty for the occurrence of various types of typhoons reduces dramatically by adaptively updating model parameters as we acquire data.

• Water for future
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• v.17 no.4
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• pp.273-279
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• 1984

An attempt is made to present a method of prediction for typhoons apporaching the Korean-peninsula. The method is based upon the Bayesian theorem to improve the observed (prior) probabilities of typhoons approaching the Korean sea area incorporating conditional probability. A total of 248 typhoons is collected and analyzed to establish prior probability and conditional probability according to the defined procedure. The typhoons used are those which encompassed the western Pacific area to which the Korean-peninsula is subjected. The results of examplary computations suggest that the presented method is promising for predicting approaching typhoons.

• Journal of Ocean Engineering and Technology
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• v.24 no.6
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• pp.7-15
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• 2010

Analyses of wind wave characteristics near the Korean marginal seas were performed in 2008 and 2009 by comparisons of an operational wind wave forecast model and ocean buoy data. In order to evaluate the model performance, its results were compared with the observed data from an ocean buoy. The model used in this study was very good at predicting the characteristics of wind waves near the Korean Peninsula, with correlation coefficients between the model and observations of over 0.8. The averaged Root Mean Square Error (RMSE) for 48 hrs of forecasting between the modeled and observed waves and storm surges/tide were 0.540 m and 0.609 m in 2008 and 2009, respectively. In the spatial and seasonal analysis of wind waves, long waves were found in July and September at the southern coast of Korea in 2008, while in 2009 long waves were found in the winter season at the eastern coast of Korea. Simulated significant wave heights showed evident variations caused by Typhoons in the summer season. When Typhoons Kalmaegi and Morakot in 2008 and 2009 approached to Korean Peninsula, the accuracy of the model predictions was good compared to the annual mean value.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.7
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• pp.892-903
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• 2020

The rapid and accurate prediction of storm-surge height during typhoon attacks is essential in responding to coastal disasters. Most methods used for predicting typhoon data are based on numerical modeling, but numerical modeling takes significant computing resources and time. Recently, various studies on the expeditious production of predictive data based on artificial intelligence have been conducted, and in this study, artificial intelligence-based storm-surge height prediction was performed. Several learning data were needed for artificial intelligence training. Because the number of previous typhoons was limited, many synthesized typhoons were created using the tropical cyclone risk model, and the storm-surge height was also generated using the storm surge model. The comparison of the storm-surge height predicted using artificial intelligence with the actual typhoon, showed that the root-mean-square error was 0.09 ~ 0.30 m, the correlation coefficient was 0.65 ~ 0.94, and the absolute relative error of the maximum height was 1.0 ~ 52.5%. Although errors appeared to be somewhat large at certain typhoons and points, future studies are expected to improve accuracy through learning-data optimization.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.3
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• pp.309-317
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• 2016

Debris flows are rapidly flowing masses of water mixed with soil and gravel from landslides which are caused by typhoons or rainstorms. The combination of Korea’s mountain dominated topography (70%) and seasonal heavy rains and typhoons causes landslides and large-scale debris flows from June to August. These phenomena often cause property damage and casualties that amount up to 20% of total annual disaster fatalities. The key point to predicting debris flow is to understand its movement mechanism, erosion, and deposition. In order to achieve a more accurate estimation of debris flow path and damage, this study incorporates quantitative analysis of high resolution LiDAR DEM (GSD 10cm) to delineate geomorphic and topographic changes induced by Jinbu real scale debris flow test.

• Spatial Information Research
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• v.17 no.3
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• pp.341-350
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• 2009

As the power and scale of typhoons are growing due to global warming and socioeconomic damages induced by super-typhoons are increasing, it is important to estimate inundation damages and to prepare proper adaptation plans against an attack of the super-typhoon. In this paper, we estimated the inundation damages of urban area around Haeundae beach induced by super-typhoons which follow the route of Typhoon Maemi with the conditions of Typhoon Vera (Ise Bay in Japan, 1959), Typhoon Durian (Philippine, 2006) and Hurricane Katrina (New Oleans in U.S.A, 2005). The coastal area around the Haeundae beach (Busan and Gyeongnam province) is expectedly damaged by severe storm surges. In this study we calculated the rise of sea level height after harmonizing the different datum levels of land and ocean and estimated the inundation depth, inundation area and the amount of building damages by using airborne LiDAR data and GIS spatial analysis techniques more accurately and quantitatively. As many researchers are predicting that super-typhoon of overwhelming power will occur around the Korean peninsula in the near future, the results of this study are expected to contribute to producing coastal inundation map and evacuation planning.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.271-278
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• 2011

Storm and flood such as torrential rains and major typhoons has often caused damages on a large scale in Korea and damages from storm and flood have been increasing by climate change and warming. Therefore, it is an essential work to maneuver preemptively against risks and damages from storm and flood by predicting the possibility and scale of the disaster. Generally the research on numerical model based on statistical methods, the KDF model of TCDIS developed by NIDP, for analyzing and predicting disaster risks and damages has been mainstreamed. In this paper, we introduced the model for prediction of damage cost from storm and flood by the neural network algorithm which outstandingly implements the pattern recognition. Also, we compared the performance of the neural network model with that of KDF model of TCDIS. We come to the conclusion that the robustness and accuracy of prediction of damage cost on TCDIS will increase by adapting the neural network model rather than the KDF model.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.884-889
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• 2005

A lot of landslides were occurred in Gangnung, Macheon and Geochang areas by Typhoons such as RUSA(2002) and MEAMI(2003). Soil properties of these areas are investigated regarding geological conditions in this study. The shallow plane failure were occurred in Gangnung and Geochang areas, whereas the deep circle failure were occurred in Macheon area. The matrix in Gangnung and Geochang areas was composed of Granite, and the matrix in Macheon area was composed of Gabbro. The disturbed and undisturbed soils were sampled in these areas. As the results of laboratory tests using sampled soils, the coefficient of permeability in Granite region is lower than that in Gabbro region. In the cases that the silt and clay contents are included less than 4% for the soils of Granite region and less than 7% for the soils of Gabbro region, the coefficients of permeability are rapidly increased for both soils. In addition, the simple equations for predicting the coefficients of permeability are proposed using the effective particle size and the silt and clay contents according to geological condition.

• Journal of Environmental Science International
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• v.32 no.8
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• pp.543-553
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• 2023

Weather forecasts and advisories provided by the national organizations in Korea that are used to identify and prevent disaster associated damage are often ineffective in reducing disasters as they only focus on predicting weather events (World Meteorological Organization(WMO ), 2015). In particular, typhoons are not a single weather disaster, but a complex weather disaster that requires advance preparation and assessment, and the WMO has established guidelines for the impact forecasting and recommends typhoon impact forecasting. In this study, we introduced the Typhoon-Ready System, which is a system that produces pre-disaster prevention information(risk level) of typhoon-related disasters across Korea and in detail for each region in advance, to be used for reducing and preventingtyphoon-related damage in Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.96-96
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• 2020

In order to establish the risk criteria of inundation due to typhoons or heavy rainfall, research is underway to predict the limit rainfall using basin characteristics, limit rainfall and artificial intelligence algorithms. In order to improve the model performance in estimating the limit rainfall, the learning data are used after the pre-processing. When 50.0% of the entire data was removed as an outlier in the pre-processing process, it was confirmed that the accuracy is over 90%. However, the use rate of learning data is very low, so there is a limitation that various characteristics cannot be considered. Accordingly, in order to predict the limit rainfall reflecting various watershed characteristics by increasing the use rate of learning data, the watersheds with similar characteristics were clustered. The algorithms used for clustering are K-Means, Agglomerative, DBSCAN and Spectral Clustering. The k-Means, DBSCAN and Agglomerative clustering algorithms are clustered at the impervious area ratio, and the Spectral clustering algorithm is clustered in various forms depending on the parameters. If the results of the clustering algorithm are applied to the limit rainfall prediction algorithm, various watershed characteristics will be considered, and at the same time, the performance of predicting the limit rainfall will be improved.