Recently, developing countries have steadily been pushing for the construction of stream-oriented smart cities, breaking away from the existing old-town-centered development in the past. Due to the accelerating effects of climate change along with such urbanization, it is imperative for urban rivers to establish a flood warning system that can predict the amount of high flow rates of accuracy in engineering, compared to using the existing Computational Fluid Dynamics (CFD) models for disaster prevention. In this study, in the case of streams where missing data existed or only small observations were obtained, the variation in flow rates could be predicted with only the appropriate deep learning models, using only limited time series flow data. In addition, the selected deep learning model allowed the minimum number of input learning data to be determined. In this study, the time series flow rates were predicted by applying the deep learning models to the Han River, which is a highly urbanized stream that flows through the capital of Korea, Seoul and has a large seasonal variation in the flow rate. The deep learning models used are Convolution Neural Network (CNN), Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Gated Recurrent Unit (GRU). Sequence lengths for time series runoff data were determined first to assess the accuracy and applicability of the deep learning models. By analyzing the forecast results of the outflow data of the Han River, sequence length for 14 days was appropriate in terms of the predicted accuracy of the model. In addition, the GRU model is effective for deep learning models that use time series data of the region with large fluctuations in flow rates, such as the Han River. Furthermore, through this study, it was possible to propose the minimum number of training data that could provide flood warning system with an effective flood forecasting system although the number of input data such as flow rates secured in new towns developed around rivers was insufficient.

To effectively deal with the spring drought facing the South Korea periodically since 2000, a monitoring system to keep tracing the drought is critical, along with the index indicating the severity of the drought in quantified value. And to conduct the in-depth evaluation of the behavior and intensity of the drought by region, a spatial analysis by area unit is needed. Satellite imagery is effective tools that provide spatial information regularly in timely manner and the vegetation index developed using band combination of satellite imagery has been used for monitoring the drought since mid-1990s. In this study, VCI and SVI were produced, using MODIS NDVI to evaluate the droughts occurred during the period, 2000 to 2007, investigating the years, severity and the regions and times where and when the drought occurred the most. Moreover, comparing with existing monitoring tool was conducted, thereby presenting the efficient method for spring drought monitoring using vegetation index.

Stream gauge stations are facilities for measuring stream water levels and flow rates, and their main purpose is to produce the data required to analyze hydrological phenomena. However, there are no specific criteria for selecting the locations and installation densities of stream gauge stations, which results in numerous problems, including regional imbalances and overlapping. To address these issues, a stream gauge network was constructed in this study considering both the transinformation of entropy (objective function 1) and the importance of each stream gauge station (objective function 2). To account for both factors, the optimal combinations that satisfied the two objective functions were determined using the Euclidean distance. Based on the rainfall runoff analysis results, unit hydrographs reflecting stream connectivity were derived and applied to entropy theory. The importance of each stream gauge station was calculated considering its purposes, such as flood control, water use, and environment. When this method was applied to the Namgang Dam Basin, it was found out that eight out of 12 stream gauge stations were required. The combination of the selected stations reflected both the transinformation of entropy and the importance of each station.

In this study, we estimate parameters of a distributed hydrologic model, GRM (grid based rainfall-runoff model), using a model-independent parameter estimation tool, PEST. We implement auto calibration of model parameters such as initial soil moisture, multipliers of overland roughness and soil hydraulic conductivity in the Geumho River Catchment and the Gamcheon Catchment using radar rainfall estimates and groundobserved rainfall represented by Thiessen interpolation. Automatic calibration is performed by GRM-MP (multiple projects), a modified version of GRM without GUI (graphic user interface) implementation, and "Parallel PEST" to improve estimation efficiency. Although ground rainfall shows similar or higher cumulative amount compared to radar rainfall in the areal average, high spatial variation is found only in radar rainfall. In terms of accuracy of hydrologic simulations, radar rainfall is equivalent or superior to ground rainfall. In the case of radar rainfall, the estimated multiplier of soil hydraulic conductivity is lower than 1, which may be affected by high rainfall intensity of radar rainfall. Other parameters such as initial soil moisture and the multiplier of overland roughness do not show consistent trends in the calibration results. Overall, calibrated parameters show different patterns in radar and ground rainfall, which should be carefully considered in the rainfall-runoff modelling applications using radar rainfall.

Monitoring tidal dynamics is imperative to disaster management because it requires a high level of precision to avert possible dangers. Good knowledge of the physical drivers of tides is vital to achieving such a precision. The Taehwa River in Ulsan City, Korea experiences tidal currents in the estuary that drains into the East Sea. The contribution of wind to tide prediction is evaluated by comparing tidal predictions using harmonic analysis and three deep learning models. Harmonic analysis is conducted on hourly water level data from 2010–2021 using the commercial pytides toolbox to generate constituents and predict tidal elevations. Three deep learning models of long short-term memory (LSTM), gated recurrent unit (GRU), and bi-directional lstm (BiLSTM) are fitted to the water level and wind speed to evaluate wind and no-wind scenarios. Results show that Taehwa tides are categorized as semidiurnal tides based on a computed form ratio of 0.2714 in a 24-h tidal cycle. The highest tidal range of 0.60 m is recorded on full moon spring tide indicating the significant lunar pull. Wind effect improved tidal prediction NSE of optimal LSTM model from 0.67 to 0.90. Knowledge of contributing effect of wind will inform flood protection measures to enhance disaster preparedness.

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: Ship are a box-shaped structure. It is used often fore and aft parts, bilge strake, deck with camber of ship structures. When this structure is compared with flat plate structure, it different to behaviour. Generally, if it subjected to axial compressive load, ultimate strength depend on the change of curvature. Also, In this paper, stiffened curved plate with 1/2+1+1/2 bay model subjected to compressive load carried out the elasto-plastic large deflection series analysis. and parameter effect considered slender ratio, web height/thickness as well as change of curvature and investigated collapse mode for analysis model.

The purpose of this study is to develop a reliable flood vulnerability index that can be used to verify scientific data. Existing flood vulnerability assessment uses methods commonly used in calculating the weight between indicators. However, because they contain similarities in their estimation measurement, such methods cannot be differentiated, and their verification is not reliable. Therefore, it is necessary to improve flood vulnerability assessment by differentiating its method from that used in existing research and to use the new method to reliably predict and evaluate the flood damage risk. In order to realize this, optimal weighting is derived by using a Bayesian network, which can make reasonable and integrated decisions after considering the existing weighting method. As a result, the integrated index for Flood Risk Assessment (InFRA) was developed through presentation of the formula. Qualitative and quantitative comparison with existing evaluation methods revealed that InFRA showed better results than previous methods and thus proved its applicability.