• Journal of Korea Water Resources Association
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• v.55 no.9
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• pp.657-668
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• 2022

With the occurrence of localized heavy rain while river flow has increased, both flow and rainfall cause riverside flood damages. As the degree of damage varies according to the level of social and economic impact, it is required to secure sufficient forecast lead time for flood response in areas with high population and asset density. In this study, the author established a flood risk matrix using ensemble rainfall runoff modeling and evaluated its applicability in order to increase the damage reduction effect by securing the time required for flood response. The flood risk matrix constructs the flood damage impact level (X-axis) using flood damage data and predicts the likelihood of flood occurrence (Y-axis) according to the result of ensemble rainfall runoff modeling using LENS rainfall data and as well as probabilistic forecasting. Therefore, the author introduced a method for determining the impact level of flood damage using historical flood damage data and quantitative flood damage assessment methods. It was compared with the existing flood warning data and the damage situation at the flood warning points in the Taehwa River Basin and the Hyeongsan River Basin in the Nakdong River Region. As a result, the analysis showed that it was possible to predict the time and degree of flood risk from up to three days in advance. Hence, it will be helpful for damage reduction activities by securing the lead time for flood response.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• The KSFM Journal of Fluid Machinery
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• v.14 no.5
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• pp.12-17
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• 2011

The seawater lift pump system is responsible for maintaining the open canal level to provide the suction flow of circulating water pump at the set point. The objective of this paper is to design a 2-stage mixed flow pump (for seawater lifting) by inverse design method and to evaluate the overall performance and the local flow fields of the pump by using a commercial CFD code. Rotating speed of the impeller is 1,750 rpm with the flow rate of 2,700 $m^3$/h. Finite volume method with structured mesh and realized k-${\varepsilon}$ turbulent model is used to guaranty more accurate prediction of turbulent flow in the pump impeller. The numerical results such as static head, brake horse power and efficiency of the mixed flow pump are compared with the design data. The simulated results are good agreement with the design data less 3% error.

• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.556-560
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• 2011

The seawater lift pump system is responsible for maintaining the open canal level to provide the suction flow of circulating water pump at the set point. The objective of this paper is to design a 2-stage mixed flow pump(for seawater lifting) by inverse design and to evaluate the overall performance and the local flow fields of the pump by using a commercial CFD code. Rotating speed of the impeller is 1,750 rpm with the flow rate of 2,700 $m^3/h$. Finite volume method with structured mesh and Realizable ${\kappa}-{\varepsilon}$ turbulent model is used to guaranty more accurate prediction of turbulent flow in the pump impeller. The numerical results such as static head brake horse power and efficiency of the mixed flow pump are compared with the reference data. Also, the periodic condition calculation method for the mixed flow pump was carried out in order to investigate the pump performance characteristics with the modification of impeller geometry.

• Fisheries and Aquatic Sciences
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• v.18 no.2
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• pp.173-181
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• 2015

We identified the $Na^+-K^+-2Cl^-$ cotransporter 2 (NKCC2) cDNA isoform from starry flounder, Platichthys stellate. The NKCC2 cDNA encoded a polypeptide of 1,043 amino acids representing 12 putative transmembrane domains based on the bioinformatic topology prediction. In addition, starry flounder NKCC2 possessed highly conserved residues within transmembrane domain 4, known as an essential site for its function. End-point reverse transcription-polymerase chain reaction analysis revealed that the NKCC2 transcript was moderately expressed only in the anterior and posterior intestines and the rectum. The NKCC2 mRNA level in the rectum, but not in other segments, was significantly induced 3 days post Streptococcus parauberis challenge, indicating that excess salt may be transported into the rectum. Taken together, our data indicate that an S. parauberis infection could tip the intestinal fluid balance in favor of fluid accumulation, indicating that bacterial pathogens can interfere with intestinal osmotic balance and normal mucosal immune homeostasis.

• Journal of the Korean GEO-environmental Society
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• v.6 no.3
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• pp.55-61
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• 2005

Dynamic compaction is a ground improvement technique which is particularly effective for loose granular soils. It has also been used successfully to the cohesive soils with high void ratio, and wastes and fills. For the design of dynamic compaction method, prediction of depth of improvement is very important. The depth of improvement is influenced not only by compaction energy but also by many parameters such as grid spacing, soil property, degree of saturation and site conditions. Based on the test results, the depth of improvement were evaluated with considering compaction energy, soil type and ground water level.

• Coupled systems mechanics
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• v.4 no.1
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• pp.67-98
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• 2015

Numerical prediction of dynamic behavior of fully coupled saturated porous media is of great importance in many engineering problems. Specifically, static and dynamic response of soils - porous media with pores filled with fluid, such as air, water, etc. - can only be modeled properly using fully coupled approaches. Modeling and simulation of static and dynamic behavior of soils require significant Verification and Validation (V&V) procedures in order to build credibility and increase confidence in numerical results. By definition, Verification is essentially a mathematics issue and it provides evidence that the model is solved correctly, while Validation, being a physics issue, provides evidence that the right model is solved. This paper focuses on Verification procedure for fully coupled modeling and simulation of porous media. Therefore, a complete Solution Verification suite has been developed consisting of analytical solutions for both static and dynamic problems of porous media, in time domain. Verification for fully coupled modeling and simulation of porous media has been performed through comparison of the numerical solutions with the analytical ones. Modeling and simulation is based on the so called, u-p-U formulation. Of particular interest are numerical dispersion effects which determine the level of numerical accuracy. These effects are investigated in detail, in an effort to suggest a compromise between numerical error and computational cost.

• Ocean Systems Engineering
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• v.1 no.1
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• pp.31-57
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• 2011

Impact pressure due to sloshing is of great concern for the ship owners, designers and builders of the LNG carriers regarding the safety of LNG containment system and hull structure. Sloshing of LNG in partially filled tank has been an active area of research with numerous experimental and numerical investigations over the past decade. In order to accurately predict the sloshing impact load, a new numerical method was developed for accurate resolution of violent sloshing flow inside a three-dimensional LNG tank including wave breaking, jet formation, gas entrapping and liquid-gas interaction. The sloshing flow inside a membrane-type LNG tank is simulated numerically using the Finite-Analytic Navier-Stokes (FANS) method. The governing equations for two-phase air and water flows are formulated in curvilinear coordinate system and discretized using the finite-analytic method on a non-staggered grid. Simulations were performed for LNG tank in transverse and longitudinal motions including horizontal, vertical, and rotational motions. The predicted impact pressures were compared with the corresponding experimental data. The validation results clearly illustrate the capability of the present two-phase FANS method for accurate prediction of impact pressure in sloshing LNG tank including violent free surface motion, three-dimensional instability and air trapping effects.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.6 no.3
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• pp.266-274
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• 1994

Two-dimensional numerical analysis is performed for the simulation of tidal characteristics related to various development projects in Incheon coastal waters along the west coast of Korea. Field observation of tides and currents was made in order to provide the input boundary and validation data set to the numerical modelling. For the simulation of changes of tides and currents a depth-integrated two-dimensional shallow water model of Flather and Heaps (1975) has been used herein. Tidal model is set up with open boundary sea level from observed two major constituents, M$_2$ and S$_2$. Subsequently the established model is utilized to investigate the effect of two development projects in this region. It has been found that in spring tide the changes of tidal amplitude are small, however, those of tidal current are locally significant.