• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• Parasites, Hosts and Diseases
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• v.48 no.3
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• pp.219-224
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• 2010

This study has been conducted to estimate the occurrence of Cryptosporidium oocysts in water supplies in the Metropolitan area of Seoul, South Korea, for 10 years from 2000 to 2009. Water samples were collected quarterly at 6 intakes in the Han River and its largest stream and 6 conventional Water Treatment Plants (WTPs) serving drinking water for 10 million people of Seoul. Cryptosporidium oocysts were found in 22.5% of intake water samples and arithmetic mean was 0.65 oocysts/10 L (range 0-22 oocysts/10 L). Although the annual mean of oocyst number was as low as 0.04-1.90 oocysts/10 L, 3 peaks in 2004 and 2007 were observed and the pollution level was a little higher in winter. The lowest density was observed at Paldang intake and the pollution level increased at Kuui and Jayang intakes. At the end of the largest stream, oocysts were found in 70% of collected samples (mean 5.71 oocysts/10 L) and it seemed that its joining the Han River resulted in the increase at Kuui intake and downstream. Oocyst removal by physical process exceeded 2.0-2.3 log and then all finished water samples collected at 6 WTPs were negative for Cryptosporidium in each 100 L sample for 10 years. These results suggested that domestic wastewater from the urban region could be a source of Cryptosporidium pollution and separating sewage systems adjacent to the intakes could be meaningful for some intakes having weakness related to parasitological water quality.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.6
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• pp.410-418
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• 2017

The stability of low-crested structure (LCS) and overtopping discharge over a seawall behind the LCS are influenced by the water level behind the structure. Hence, the experimental results can be distorted unless the increase of water level is known when two-dimensional experiment is carried out. In order to estimate increase of the mean water level behind the low-crested structure, this study applied a hybrid technique that combined results of two-dimensional model test and hydrodynamic numerical modeling based on the relationship between the water level and discharge. By using this technique, the mean water level increase and flow field can be obtained almost at the same time, which resolved the above problem considerably. In addition, this method can provide an approximate information about the outflow velocity from the openings of the structure, which is helpful for selecting appropriate planar configuration of the low-crested structure.

• Journal of Ocean Engineering and Technology
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• v.16 no.2
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• pp.6-9
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• 2002

The long-term monthly variations of tide with tidal harmonic analysis in Pusan Harbour are investigated. The present spring tidal range decreased 1.4 cm and the variation of phase lag increased than 1974. The high and low water level of yearly mean sea level is show during the February to March and August to September, respectively. It is important to note that the larger lunar elliptic N2 is large in comparison with lunisolar diurnal K1 and principal lunar diurnal O1. The ratios (Correction Factors) of monthly mean sea level and the main 4 tidal constituents are evaluated to correct the shortly (monthly) observed tide for the design of harbour facilities.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.6
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• pp.335-349
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• 2017

Submerged breakwaters installed under the water surface are a representative coastal structure to prevent coastal erosion, and various types of submerged breakwaters have been proposed and discussed so far. Generally, submerged breakwaters make the complex wave fields due to abrupt change in water depth at the crown of the breakwater. In this study, wave heights and mean water level formed around a breakwater are examined numerically for three-dimensional permeable submerged breakwaters. OLAFOAM, CFD open source code, is applied in the numerical analysis, and the comparisons are made with available experimental results on the permeable upright wall and the impermeable submerged breakwater to verify its applicability to the three-dimensional numerical analysis. Based on the applicability of OLAFOAM numerical code, the wave height and mean water level distribution formed around the permeable submerged breakwaters are investigated under the formation condition of salient. The numerical results show that as the gap width between breakwaters decreases, the wave height in the center of the gap increases, while it decreases behind the gap, and the installing position of the breakwater from the shoreline has little influence on the change of the wave height. Furthermore, it is found that the decrease of the mean water level near the gap between breakwaters increases with decreasing of the gap width.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.723-730
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• 2019

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.

• Structural Engineering and Mechanics
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• v.86 no.5
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• pp.621-633
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• 2023

Stability analysis and support system estimation of the Beheshtabad water transmission tunnel is investigated in this research. A combination approach based on the rock mass rating (RMR) and rock mass quality index (Q) is used for this purpose. In the first step, 40 datasets related to the petrological, structural, hydrological, physical, and mechanical properties of tunnel host rocks are measured in the field and laboratory. Then, RMR, Q, and height of influenced zone above the tunnel roof are computed and sorted into five general groups to analyze the tunnel stability and determine its support system. Accordingly, tunnel stand-up time, rock load, and required support system are estimated for five sorted rock groups. In addition, various empirical relations between RMR and Q i.e., linear, exponential, logarithmic, and power functions are developed using the analysis of variance (ANOVA). Based on the significance level (sig.), determination coefficient (R²) and Fisher-test (F) indices, power and logarithmic equations are proposed as the optimum relations between RMR and Q. To validate the proposed relations, their results are compared with the results of previous similar equations by using the variance account for (VAF), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) indices. Comparison results showed that the accuracy of proposed RMR-Q relations is better than the previous similar relations and their outputs are more consistent with actual data. Therefore, they can be practically utilized in designing the tunneling projects with an acceptable level of accuracy and reliability.

• Journal of the Korean Society of Safety
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• v.35 no.2
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• pp.28-33
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• 2020

Recently, the overseas construction market has been actively promoted for about 10 years, and overseas dam construction has been continuously performed. For the economic and safe construction of the dam, it is important to prepare the main dam construction plan considering the design frequency of the diversion tunnel and the cofferdam. In this respect, the prediction of river level during the rainy season is significant. Since most of the overseas dam construction sites are located in areas with poor infrastructure, the most efficient and economic method to predict the water level in dam construction is to use the upstream water level. In this study, a linear regression model, which is one of the simplest statistical methods, was proposed and examined to predict the downstream level from the upstream level. The Pyeongchang River basin, which has the characteristics of the upper stream (mountain stream), was selected as the target site and the observed water level in Pyeongchang and Panwoon gaging station were used. A regression equation was developed using the water level data set from August 22th to 27th, 2017, and its applicability was tested using the water level data set from August 28th to September 1st, 2018. The dependent variable was selected as the "level difference between two stations," and the independent variable was selected as "the level of water level in Pyeongchang station two hours ago" and the "water level change rate in Pyeongchang station (m/hr)". In addition, the accuracy of the developed equation was checked by using the regression statistics of Root Mean Square Error (RMSE), Adjusted Coefficient of Determination (ACD), and Nach Sutcliffe efficiency Coefficient (NSEC). As a result, the statistical value of the linear regression model was very high, so the downstream water level prediction using the upstream water level was examined in a highly reliable way. In addition, the results of the application of the water level change rate (m/hr) to the regression equation show that although the increase of the statistical value is not large, it is effective to reduce the water level error in the rapid level rise section. Accordingly, this is a significant advantage in estimating the evacuation water level during main dam construction to secure safety in construction site.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.903-911
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• 2022

Groundwater, one of the resources for supplying water, fluctuates in water level due to various natural factors. Recently, research has been conducted to predict fluctuations in groundwater levels using Artificial Neural Network (ANN). Previously, among operators in ANN, Gradient Descent (GD)-based Optimizers were used as Optimizer that affect learning. GD-based Optimizers have disadvantages of initial correlation dependence and absence of solution comparison and storage structure. This study developed Gradient Descent combined with Harmony Search (GDHS), a new Optimizer that combined GD and Harmony Search (HS) to improve the shortcomings of GD-based Optimizers. To evaluate the performance of GDHS, groundwater level at Icheon Yullhyeon observation station were learned and predicted using Multi Layer Perceptron (MLP). Mean Squared Error (MSE) and Mean Absolute Error (MAE) were used to compare the performance of MLP using GD and GDHS. Comparing the learning results, GDHS had lower maximum, minimum, average and Standard Deviation (SD) of MSE than GD. Comparing the prediction results, GDHS was evaluated to have a lower error in all of the evaluation index than GD.

• Korean Journal of Exercise Nutrition
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• v.23 no.4
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• pp.1-5
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• 2019

[Purpose] The goal of this study was to review data on physical activity level (PAL), a crucial index for determining estimated energy requirement (EER), calculated as total energy expenditure (TEE, assessed with doubly labeled water [DLW]) divided by resting metabolic rate (RMR, PAL = TEE/RMR) in female athletes and to understand the methods of assessing athletes' EERs in the field. [Methods] For the PAL data review among female athletes, we conducted a PubMed search of the available literature related to the DLW method. DLW studies measuring TEE and RMR were included for the present review. [Results] Briefly, the mean PAL was 1.71 for collegiate swimmers with moderate training, which was relatively low, but the mean PAL was 3.0 for elite swimmers during summer training camp. This shows that PAL can largely vary even within the same sport depending on the amount of training, and the differences in PAL were remarkable depending on the sport. Aside from the DLW method, there is currently no research tool related to athletes' EERs that can be used in the field. [Conclusion] Briefly, the mean PAL was 1.71 for collegiate swimmers with moderate training, which was relatively low, but the mean PAL was 3.0 for elite swimmers during summer training camp. This shows that PAL can largely vary even within the same sport depending on the amount of training, and the differences in PAL were remarkable depending on the sport. Aside from the DLW method, there is currently no research tool related to athletes' EERs that can be used in the field.