• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.6
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• pp.979-994
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• 2015

The mode shapes of a segmented hull model towed in a model basin were predicted using both the Proper Orthogonal Decomposition (POD) and cross random decrement technique. The proper orthogonal decomposition, which is also known as Karhunen-Loeve decomposition, is an emerging technology as a useful signal processing technique in structural dynamics. The technique is based on the fact that the eigenvectors of a spatial coherence matrix become the mode shapes of the system under free and randomly excited forced vibration conditions. Taking advantage of the simplicity of POD, efforts have been made to reveal the mode shapes of vibrating flexible hull under random wave excitation. First, the segmented hull model of a 400 K ore carrier with 3 flexible connections was towed in a model basin under different sea states and the time histories of the vertical bending moment at three different locations were measured. The measured response time histories were processed using the proper orthogonal decomposition, eventually to obtain both the first and second vertical vibration modes of the flexible hull. A comparison of the obtained mode shapes with those obtained using the cross random decrement technique showed excellent correspondence between the two results.

• Journal of Korean Society on Water Environment
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• v.27 no.5
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• pp.587-596
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• 2011

The interest in hydrological modeling has increased significantly recently due to the necessity of watershed management, specifically in regards to lumped models, which are being prosperously utilized because of their relatively uncomplicated algorithms which require less simulation time. However, lumped models require empirical coefficients for hydrological analyses, which do not take into consideration the heterogeneity of site-specific characteristics. To overcome such obstacles, a distributed model was offered as an alternative and the number of researches related to watershed management and distributed models has been steadily increasing in the recent years. Thus, in this study, the feasibility of a grid-based rainfall-runoff model was reviewed using the flood runoff process in the Han River basin, including the ChungjuDam, HoengseongDam and SoyangDam watersheds. Hydrological parameters based on GIS/RS were extracted from basic GIS data such as DEM, land cover, soil map and rainfall depth. The accuracy of the runoff analysis for the model application was evaluated using EFF, NRMSE and QER. The calculation results showed that there was a good agreement with the observed data. Besides the ungauged spatial characteristics in the SoyangDam watershed, EFF showed a good result of 0.859.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.127-127
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• 2021

In order to estimate parameter uncertainty of hydrological models, the consideration of the likelihood functions which provide reliable parameters of model is necessary. In this study, the Bayesian Markov Chain Monte Carlo (MCMC) method with informal likelihood functions is used to analyze the uncertainty of parameters of the SURR model for estimating the hourly streamflow of Gunnam station of Imjin basin, Korea. Three events were used to calibrate and one event was used to validate the posterior distributions of parameters. Moreover, the performance of four informal likelihood functions (Nash-Sutcliffe efficiency, Normalized absolute error, Index of agreement, and Chiew-McMahon efficiency) on uncertainty of parameter is assessed. The indicators used to assess the uncertainty of the streamflow simulation were P-factor (percentage of observed streamflow included in the uncertainty interval) and R-factor (the average width of the uncertainty interval). The results showed that the sensitivities of parameters strongly depend on the likelihood functions and vary for different likelihood functions. The uncertainty bounds illustrated the slight differences from various likelihood functions. This study confirms the importance of the likelihood function selection in the application of Bayesian MCMC to the uncertainty assessment of the SURR model.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.136-136
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• 2022

This study investigates the possibility of coupling empirical mode decomposition (EMD) for runoff prediction from machine learning (ML) models. Here, support vector regression (SVR) and convolutional neural network (CNN) were considered for ML algorithms. Precipitation (P), minimum temperature (Tmin), maximum temperature (Tmax) and their intrinsic mode functions (IMF) values were used for input variables at a monthly scale from Jan. 1973 to Dec. 2020 in the Grand river basin, Canada. The support vector machine-recursive feature elimination (SVM-RFE) technique was applied for finding the best combination of predictors among input variables. The results show that the proposed method outperformed the individual performance of SVR and CNN during the training and testing periods in the study area. According to the correlation coefficient (R), the EMD-SVR model outperformed the EMD-CNN model in both training and testing even though the CNN indicated a better performance than the SVR before using IMF values. The EMD-SVR model showed higher improvement in R value (38.7%) than that from the EMD-CNN model (7.1%). It should be noted that the coupled models of EMD-SVR and EMD-CNN represented much higher accuracy in runoff prediction with respect to the considered evaluation indicators, including root mean square error (RMSE) and R values.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.1
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• pp.89-97
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• 2006

Soil erosion has caused serious environmental problems which threaten the foundation of natural resources. In this paper, we chose RUSLE erosion model, which could be connected easily with GSIS and available generally in mid-scale watershed among soil erosion models, and extracted factors entered model by using GSIS spatial analysis method. First, this study used GIS database as soil map, DEM, land cover map and rainfall data of typhoon Memi (2003) to analyze soil loss amount of Dam basin. To analyze the changes of soil loss in considering basin characteristics as up-, mid- and downstream, this study calculated soil erodibility factor (K), topographic factors (LS), and cover management factor (C). As a result of analysis, K and LS factors of upstream showed much higher than those of downstream because of the high ratio of forest. But C factor of downstream showed much higher than that of upstream because of the high ratio of agricultural area. As a result of analysis of soil loss, unit soil loss of upstream is 4.3 times than soil loss of downstream. Therefore, the establishment of countermeasures for upstream is more efficient to reduce soil loss.

• The Journal of Engineering Geology
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• v.10 no.3
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• pp.263-272
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• 2000

The main objective of this study is to simulate the rainfall-runoff relationship of the Ohwon rivet basin. For the this study, we used GIS technique and HMS(Hydrological Modeling System). In this study, watershed itself and geometric factors of watershed are extracted from DEM by using a GIS technique. The scanned data of topographical map with scale of 1:50,000 in the Ohwon river basin is used to this study and it is converted to DEM data. The parameters of Hydrological Modeling System as watershed area(A), river length, SCS Curve Number(CN) etc. are extracted by using the GIS technique in the Ohwon Basin. Extracted parameters are applied to the Hydrological Model System, then the paramenters optimized by the observed data and rainfall data. Then, the optimized parameters and Hydrological Modeling System are applied to the study area for the simulation of rainfall-runoff relationship. With the resultn of this study, GIS technique is useful to the extraction of watershed characteristics factors and Hydrological Modeling System is successful to the simulation of rainfall-runoff relationship.

• Journal of Korea Water Resources Association
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• v.45 no.3
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• pp.243-252
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• 2012

The objective of this study is to analyze lateral inflow hydrologically. The IUH of lateral inflow is sum of the impulse responses of total cells in basin. This IUH bases on the Muskingum channel routing method, which hydrologically re-analysed to represent it as a linear combination of the linear channel model considering only the translation and the linear reservoir model considering only the storage effect. Rectangular and triangular basins were used as imaginary basins and IUH of each basin were derived. The derived IUH have different characteristics with respect to basin's shape. The storage coefficient of lateral inflow was also derived mathematically using general definitions of concentration time and storage coefficient. As a result, the storage coefficient of lateral inflow could be calculated easily using basin's width, length and hydrological characteristics of channel.

• Journal of Korea Water Resources Association
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• v.45 no.11
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• pp.1121-1130
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• 2012

Projected changes and their impacts on water quality are simulated in response to climate change stressors. CGHR (T63) simulation on the A1B scenario is converted to regional scale data using a statistical down-scaling method and applied to SWAT model to assess water quality impacts in Nakdong River basin. The results demonstrate that rainfall-runoff and pollutant loading in the future (2011~2100) will clearly increase as compared to the last 30-year average. The rate of pollutant loading increase is expected to continue its acceleration until 2040s. Runoff also shows similar patterns to the precipitation, increasing by 60%. Accordingly, the runoff increase results in escalation of pollutant loading by 35~45% for TSS and 5~20% for T-P. This phenomenon is more pronounced in the upper basin during winter and spring season.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.1
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• pp.49-56
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• 2017

Recently, many research works on the icebreaking vessels have been published as the possibility of passing Arctic routes has been increasing. The model ship test on the pack ice model in the ice basin is actively carried out as a way to investigate the performance of icebreaking vessels. In this test, the concentration of pack ice is important since it directly affects the performance. However, it is difficult to measure the concentration because not only the pack ice has uneven shape but also it keeps floating around in the basin. In this paper, an algorithm to identify the concentration of pack ice is introduced. From a digital image of pack ice obtained in the ice basin, the goal is to measure the area of pack ice using an image processing technique. Instead of the general global binarization that yields numerical errors in this problem, a local binarization technique, coupled with image subdivision based on the quadtree structure, is developed. The concentration results obtained by the developed algorithm are compared with the manually measured data to prove its accuracy.