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http://dx.doi.org/10.26748/KSOE.2019.066

Study of the Construction of a Coastal Disaster Prevention System using Deep Learning  

Kim, Yeon-Joong (Department of Civil and Urban Engineering Inje University)
Kim, Tae-Woo (Department of Civil and Urban Engineering Inje University)
Yoon, Jong-Sung (Department of Civil and Urban Engineering Inje University)
Kim, Myong-Kyu (Hydro Technology Institute Co. Ltd.)
Publication Information
Journal of Ocean Engineering and Technology / v.33, no.6, 2019 , pp. 590-596 More about this Journal
Abstract
Numerous deaths and substantial property damage have occurred recently due to frequent disasters of the highest intensity according to the abnormal climate, which is caused by various problems, such as global warming, all over the world. Such large-scale disasters have become an international issue and have made people aware of the disasters so they can implement disaster-prevention measures. Extensive information on disaster prevention actively has been announced publicly to support the natural disaster reduction measures throughout the world. In Japan, diverse developmental studies on disaster prevention systems, which support hazard map development and flood control activity, have been conducted vigorously to estimate external forces according to design frequencies as well as expected maximum frequencies from a variety of areas, such as rivers, coasts, and ports based on broad disaster prevention data obtained from several huge disasters. However, the current reduction measures alone are not sufficiently effective due to the change of the paradigms of the current disasters. Therefore, in order to obtain the synergy effect of reduction measures, a study of the establishment of an integrated system is required to improve the various disaster prevention technologies and the current disaster prevention system. In order to develop a similar typhoon search system and establish a disaster prevention infrastructure, in this study, techniques will be developed that can be used to forecast typhoons before they strike by using artificial intelligence (AI) technology and offer primary disaster prevention information according to the direction of the typhoon. The main function of this model is to predict the most similar typhoon among the existing typhoons by utilizing the major typhoon information, such as course, central pressure, and speed, before the typhoon directly impacts South Korea. This model is equipped with a combination of AI and DNN forecasts of typhoons that change from moment to moment in order to efficiently forecast a current typhoon based on similar typhoons in the past. Thus, the result of a similar typhoon search showed that the quality of prediction was higher with the grid size of one degree rather than two degrees in latitude and longitude.
Keywords
Disaster prevention system; Artificial intelligence; Deep learning; Big data;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 Hitokoto, M., Sakuraba, M., and Sei, Y., 2016. Development of the Real-Time River Stage Predicition Method using Deep Learning. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 72(4), I_187-I_192. https://doi.org/10.2208/jscejhe.72.I_187   DOI
2 Nakatani. Y., Ishizaki. M., Nishida. S., 2017. Estimation of Water Quality Variation in a Tidal River by Applying Deep Learning Models. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 73(4), I_1141-I_1146. https://doi.org/10.2208/jscejhe.73.I_1141   DOI
3 Typhoon Research center, 2019. Typhoon Information. [Online] Available at: [Accessed May. 2019].
4 National Institute of Informatics (NII), 2019. Digital Typhoon. [Online] Available at: [Accessed June 2019].
5 Srivastava. N., Hinton. G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., 2014. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 1929-1958.
6 Japan Meteorological Agency (JMA), 2019. Numerical Weather Prediction Activities Service System. [Online] Available at: [Accessed June. 2019].
7 Kim, Y-J., Tanaka, K., Nakashima, H., Nakakita, E., 2015. Debris Flow Prevention Countermeasures with Urban Inundation in a Multihazard-Environment. International Journal of Erosion Control Engineering, 9(2), 58-67. https://doi.org/10.13101/ijece.9.58
8 Kim, Y-J., Kim, T-W,, Yoon, J-S,, Kim, I-H., 2019. Study on Prediction of Similar Typhoons through Neural Network Optimization. Journal of Ocean Engineering and Technology, 33(5), 427-434. https://doi.org/10.26748/KSOE.2019.065   DOI
9 Sugiura. M., Tsujikura. H., Tanaka. K., 2015. Interpretation on the Temporal Change of Parameters in the Flood Prediction Model based on the Reserve Function Method. Japan Society of Civil Engineers, 71(4), I_307-I_312. https://doi.org/10.2208/jscejhe.71.I_307