• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.6
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• pp.15-24
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• 2005

The normalization on the characteristics of water level change at the upstream gauging station was attempted according to the operation of drainage sluice gate of the Geumgang estuary dam. The characteristics were normalized by the analysis of water level change and by the linear-regression of the water level data measured at the inner station of Geumgang estuary dam and upstream gauging station. The results of normalization may be referred to the management of Geumgang estuary lake, the operation of pumping and drainage stations in the shore of the lake. The mean response time of water level change on Ibpo, Ganggyeong and Gyuam water level station were 39,81 and 160 minutes, when sluice gate was opened respectively. The mean velocity of surface wave, the mean displacement of water level change, the mean time of water level change and the mean rate of water level change varied largely depending on the location of gauging station and the characteristics of stream section of the water level gauging station.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1245-1254
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• 2020

In this study, the limitations of the existing water level detection algorithm using CCTV images were recognized and the water level detection algorithm was improved by applying the Region growing technique. It applied three techniques (Horizontal projection profile, Texture analysis, and Optical flow) to estimate the water area, and the results were analyzed in a comprehensive analysis to select the initial water area. The water level was then continuously detected by the Region growing technique, referring to the initial water area. As a result, it was possible to confirm that the exact level of water was detected without being affected by environmental factors compared to the existing level detection algorithm, which had frequent mis-detection phenomena depending on the surrounding environmental factors. In addition, the water level was detected in the video showing flooded roads in urban areas, not in the video of the river. These results are believed to be able to supplement the difficulty of monitoring at all times with limited manpower by automatically detecting the level of water through numerous CCTV footage installed throughout the country, and to contribute to laying the foundation for preventing disasters caused by torrential rains and typhoons in advance.

• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.2
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• pp.13-23
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• 2006

In Korea, upland irrigation generally depends on the ground water or natural rainfall since irrigation water supplied from dams is mainly used for paddy irrigation, and only limited amount of irrigation water is supplied to the upland area. For the stable security of upland irrigation water, storage level of irrigation dams was simulated by the periods. A year was divided into 4 periods considering the irrigation characteristics. Through the periodical management of storage level, water utilization efficiency in irrigation dams could be enhanced and it makes available to secure extra available water from existing dams without new development of water resources. Two study areas, Seongju and Donghwa dam, were selected for this study. Runoff from the watersheds was simulated by the modified tank model and the irrigation water to upland crops was calculated by the Penman-Monteith method. The analyzed results showed that relatively sufficient extra available water could be secured for the main upland crops in Seongju area. In case of Donghwa area, water supply to non-irrigated upland was possible in normal years but extra water was necessary in drought years such as 1998 and 2001.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.107-120
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• 2019

Reservoir water level data identify the current water storage of the reservoir, and they are utilized as primary data for management and research of agricultural water. For the reservoir storage management, Korea Rural Community Corporation (KRC) installed water level stations at around 1,600 agricultural reservoirs and has been collecting the water level data every 10 minutes. However, various kinds of outliers due to noise and erroneous problems are frequently appearing because of environmental and physical causes. Therefore, it is necessary to detect outlier and improve the quality of reservoir water level data to utilize the water level data in purpose. This study was conducted to detect and classify outlier and normal data using two different models including the threshold model and the artificial neural network (ANN) model. The results were compared to evaluate the performance of the models. The threshold model identifies the outlier by setting the upper/lower bound of water level data and variation data and by setting bandwidth of water level data as a threshold of regarding erroneous water level. The ANN model was trained with prepared training dataset as normal data (T) and outlier (F), and the ANN model operated for identifying the outlier. The models are evaluated with reference data which were collected reservoir water level data in daily by KRC. The outlier detection performance of the threshold model was better than the ANN model, but ANN model showed better detection performance for not classifying normal data as outlier.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.31-31
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• 2023

During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.

• Journal of Korea Water Resources Association
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• v.55 no.4
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• pp.279-289
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• 2022

Since the frequency of heavy rainfall is increasing due to climate change, water levels in the river exceed past historical records. The rating-curve is to convert water level into flow dicscharge from the regression analysis of the water level and corresponding flow discharges. However, the rating-curve involves many uncertainties because of the limited data especially when observed water level exceed past historical water levels. In order to compensate for insufficient data and increase the accuracy of flow discharge data, this study estimates the flow discharge in the river computed mathematically using Monte Carlo simulation based on a 1D hydrodynamic numerical model. Based on the existing rating curve, a random combination of coefficients constituting the rating-curve creates a number of virtual rating curve. From the computed results of the hydrodynamic model, it is possible to estimate flow discharge which reproduces best fit to the observed water level. Based on the statistical evaluation of these samples, a method for mathematically estimating the water level and flow discharge of all cross sections is porposed. The proposed methodology is applied to the junction of Yochoen Stream in the Seomjin River. As a result, it is confirmed that the water level reproducibility was greatly improved. Also, the water level and flow discharge can be calculated mathematically when the proposed method is applied.

• Journal of Environmental Science International
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• v.14 no.4
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• pp.405-412
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• 2005

Groundwater in Jeju Island, flowing through main stream, is spring water from underground. To set a fixed quantity of groundwater flowing from surface in a hydrological view, 4 downstream (Woedo stream, Gangjung stream, Yeonwoe stream and Ongpo stream) were selected to calculate the characteristic of baseflow and the base-flow discharge through the data on tachometry. There were 11 to 14 level peak caused by runoff, mostly occurred during monsoon season. Also, duration of runoff was 15 to 25 hours, well reflecting the characteristic of inclined, short stream length in Jeju Island and pervious hydrogeographical feature. In case of Gangjung stream, Yeonwoe stream and Ongpo stream, variation of stream water level by baseflow rose above during summer, which was closely linked to the distribution of seasonal precipitation. From autumn to spring, water level fell below while that of Woedo stream remained the same all year round. Data on the water level observed in Woedo stream and Gangjung stream in every single minutes was applied to weir formula(equation of Oki and Govinda Rao) to calculate baseflow discharge. Also, using the data on current and water level calculated in Ongpo stream and Yeonwoe stream, water level-water flow rating was applied to assess base flow discharge.

• Journal of People, Plants, and Environment
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• v.24 no.5
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• pp.469-483
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• 2021

Background and objective: If the Nelumbo nucifera spreads in a wetland at a high density, it can have considerable positive and negative ecological effects on habitats. For this reason, it is necessary to precisely investigate the impacts of its rapid proliferation. This study was conducted to propose the distribution and management of N. nucifera, which can cause the degradation of wildlife habitats due to the rapid spread of internal and external environmental factors that may affect the Junam wetland ecosystem. Methods: For the investigation and analysis of physical and ecological characteristics, factors of the abiotic environment such as general weather conditions, topography and water depth structure, and soil and water quality analysis, and bioenvironment characteristics such as changes in the N. nucifera community distribution were evaluated. To assess whether the differences in the soil depth and physicochemical characteristics between the N. nucifera community and the aquatic plant community are statistically significant, a One-way ANOVA was executed. Results: N. nucifera was presumably introduced in approximately 2007 and observed at a prevalence of only 0.8% in 2009, but had expanded to 11.1% in 2014. After that, the area was increased to 19.3% in 2015 and 40.0% in 2017, about twice that of the previous survey year. The rapid diffusion of an N. nucifera colony can have adverse effects on wildlife habitats and biodiversity at Junam Wetland. To solve these problems, four management methods can be proposed; water level management, mowing management, installation of posts and removal of lotus roots. Control of the N. nucifera community using these methods was judged to be suitable for cutting and water level management when considering expansion rate, water level variation, and wildlife habitat impacts. Conclusion: As the biotic and abiotic environmental factors are different for each wetland, it is necessary to determine the timing and method of management through a detailed investigation.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.6
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• pp.667-683
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• 2015

Since aged water treatment facilities could threaten the sustainable water supply, asset management system has been adopted for their systematic management. Level of Service(LoS) is one of critical components of asset management and could be quantified through benchmark index(BMI). Water supplier could estimate consumer's satisfaction and their performance through BMI to improve the LoS. We developed BMI for water treatment facilities from customer's satisfaction survey. BMI, represented with the Total Service Score(TSS), was assessed with water quality, water pressure, taste and odor, water rate, and service quality with weighing factors. BMI could, further, be used to assist the analysis of the life cycle cost to increase the unit of LoS.

• Smart Media Journal
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• v.11 no.5
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• pp.56-62
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• 2022

Water reservoir is defined as a storage space to hold and supply filtered water and it's significantly important to manage water level in the water reservoir so as to stabilize water supply by controlling water supply depending on demand. Liquid level sensors have been installed in the water reservoir and the pumps in the booster station facilitated management for optimum water level in the water reservoir. But the incident responses including sensor malfunction and communication breakdown actually count on manager's inspection, which involves risk of accidents. To stabilize draining facility management, this study has come up with AI model that predicts changes in the water level in the water reservoir. Going through simulation in the case of missing data in the water level to verify stability in relation to the field application of the prediction model for water level changes in the reservoir, the comparison of actual change value and predicted value allows to test utility of the model.