The coastal area of Japan has been damaged yearly by storm surges and flooding disasters in the past, including those associated with typhoons. In addition, the scale of damage is increasing rapidly due to the changing global climate and environment. As disasters due to storm surges become increasingly unpredictable, more measures should be taken to prevent serious damage and casualties. The Japanese government published a hazard map manual in 2015 and obligates the creation of a hazard map based on a parametric model as a measure to reduce high-scale storm surges. Parametric model (typhoon model) accounting for the topographical influences of the surroundings is essential for calculating the wind field of a typhoon. In particular, it is necessary to calculate the wind field using a parametric model in order to simulate a virtual typhoon (the largest typhoon) and to improve the reproducibility. Therefore, in this study, the aim was to establish a hazard map by assuming storm surges of the largest scale and to propose a parametric model that considers the changing shape of typhoons due to topography. The main objectives of this study were to analyze the characteristics of typhoons due to pass through Japan, to develop a parametric model using a combination of Holland’s and Myers’s models that is appropriate for the largest scale of typhoon, and to analyze the parameters of Holland’s model using grid point values (GPVs). Finally, we aimed to propose a method that considers the changing shape of typhoons due to topography. The modeling outcomes of tide levels and storm surge heights show that the reproduced results obtained by the analysis method proposed in this study are more accurate than those obtained using GPVs. In addition, the reproducibility of the proposed model was evaluated showing the high and excellent reproducibility of storm surge height according to the geographic characteristics.

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Artificial intelligence (AI)-aided research currently enjoys active use in a wide array of fields thanks to the rapid development of computing capability and the use of Big Data. Until now, forecasting methods were primarily based on physics models and statistical studies. Today, AI is utilized in disaster prevention forecasts by studying the relationships between physical factors and their characteristics. Current studies also involve combining AI and physics models to supplement the strengths and weaknesses of each aspect. However, prior to these studies, an optimization algorithm for the AI model should be developed and its applicability should be studied. This study aimed to improve the forecast performance by constructing a model for neural network optimization. An artificial neural network (ANN) followed the ever-changing path of a typhoon to produce similar typhoon predictions, while the optimization achieved by the neural network algorithm was examined by evaluating the activation function, hidden layer composition, and dropouts. A learning and test dataset was constructed from the available digital data of one typhoon that affected Korea throughout the record period (1951-2018). As a result of neural network optimization, assessments showed a higher degree of forecast accuracy.

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The swash zone is an area that causes a change in the shape of a beach by generating sediment transport under the influence of intermittent waves, where wave run-up and run-down are infinitely repeated in the final stage of the shoaling process. However, the ability to predict the sediment transport is extremely poor despite the swash zone being an extremely important area in terms of offshore disaster prevention. In particular, many researchers are conducting studies on the development of various types of observation equipment and analysis techniques because the turbulent flow of active fluid dominates the sediment transport and is an extremely important parameter for the analysis of the transport mechanism. However, in flow velocity measurement, it is difficult to measure a quantitative representative flow velocity over time because the swash zone has a shallow water depth and an active turbulent flow. Expensive equipment and short-time measurement are also limitations. Therefore, the purpose of this study was to evaluate the applicability of nonintrusive space-time image velocimetry(STIV) to analyze the flow characteristics of fluid in the swash zone, such as the movement velocity and period of intermittent waves in the shoaling process. The prediction accuracy was improved by removing various noises included in the images with the introduction of artificial intelligence for immediate and accurate calculation of the representative flow velocity using images that can be obtained easily. Consequently, it was discovered that the spatial representative flow velocity occurring in the swash zone, change in the wave period according to the shoaling effect, rip current and surface velocity can be measured.