• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1195-1204
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• 2021

Recently, local torrential rain have become more frequent and severe due to abnormal climate conditions, causing a surge in human and properties damage including infrastructures along the river. In this study, water surface elevation prediction algorithm was developed using the LSTM (Long Short-term Memory) technique specialized for time series data among Machine Learning to estimate and prevent flooding of the facilities. The study area is Jamsu Bridge, the study period is 6 years (2015~2020) of June, July and August and the water surface elevation of the Jamsu Bridge after 3 hours was predicted. Input data set is composed of the water surface elevation of Jamsu Bridge (EL.m), the amount of discharge from Paldang Dam (m³/s), the tide level of Ganghwa Bridge (cm) and the number of tweets in Seoul. Complementary data were constructed by using not only structured data mainly used in precedent research but also unstructured data constructed through wordcloud, and the role of unstructured data was presented through comparison and analysis of whether or not unstructured data was used. When predicting the water surface elevation of the Jamsu Bridge, the accuracy of prediction was improved and realized that complementary data could be conservative alerts to reduce casualties. In this study, it was concluded that the use of complementary data was relatively effective in providing the user's safety and convenience of riverside infrastructure. In the future, more accurate water surface elevation prediction would be expected through the addition of types of unstructured data or detailed pre-processing of input data.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1285-1294
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• 2021

In recent years, as rainfall is concentrated and rainfall intensity increases worldwide due to climate change, the scale of flood damage is increasing. Rainfall of a previously unobserved magnitude falls, and the rainy season lasts for a long time on record. In particular, these damages are concentrated in ASEAN countries, and at least 20 million people among ASEAN countries are affected by frequent flooding due to recent sea level rise, typhoons and torrential rain. Korea supports the domestic flood warning system to ASEAN countries through various ODA projects, but the communication network is unstable, so there is a limit to the central control method alone. Therefore, in this study, an artificial intelligence-based flood prediction model was developed to develop an observation station that can observe water level and rainfall, and even predict and warn floods at once at one observation station. Training, validation and testing were carried out for 0.5, 1, 2, 3, and 6 hours of lead time using the rainfall and water level observation data in 10-minute units from 2009 to 2020 at Junjukbi-bridge station of Seolma stream. LSTM was applied to artificial intelligence algorithm. As a result of the study, it showed excellent results in model fit and error for all lead time. In the case of a short arrival time due to a small watershed and a large watershed slope such as Seolma stream, a lead time of 1 hour will show very good prediction results. In addition, it is expected that a longer lead time is possible depending on the size and slope of the watershed.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.163-167
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• 2007

For numerical analysis of a tidal flow, a two-dimensional hydrodynamic model is developed by solving the nonlinear shallow-water equations. The governing equations are discretized explicitly with a finite difference leap-frog scheme and a first-order upwind scheme on adaptive hierarchical quadtree grids. The developed model is verified by applying to prediction of tidal behaviors. The calculated tidal levels are compared to available field measurements. A very reasonable agreement is observed.

• Journal of the Korean Solar Energy Society
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• v.30 no.5
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• pp.100-106
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• 2010

The purpose of this study is to predict the effective sky temperature on Osan City during the summer. The north latitude, east longitude of Osan City is $37^{\circ}06'$ and $127^{\circ}02'$. The altitude from the sea level is 48m. Empirical relations of the effective sky temperature suggested by Duffie and Beckman are compared on clear days. For the effective sky temperature prediction, data measured by the Korea Meteorological Administration is used as an input to the Bliss model. Both Hottel and Krondratyev model are used to calculate the water vapor emissivity. The results using Hottel's model match well with the empirical relation proposed by Bliss. The results show maximum, minimum, and average values depending on water vapor emissivity model. The maximum deviation is about 10K and is due to total emissivity model.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2001.09a
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• pp.168-171
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• 2001

It is a common practice to extract water from the ground for domestic, agricultural or industrial uses or to lower the groundwater level for construction work. An accurate prediction of ground settlement Is sometimes crucial when groundwater is pumped. This case study have shown that drawdown of the groundwater table may cause ground subsidence. Many settlement gauges was installed in the vicinity of a pumped well to measure the surface settlement. The relationships between the level of groundwater drop and surface settlement is investigated In this research.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.15 no.3
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• pp.57-62
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• 2007

In this paper, we attempted to produce the cloud forecast that use the numerical weather prediction(NWP) MM5 for objective cloud forecast. We presented two methods for cloud forecast. One of them used total cloud mixing ratio registered to sum(synthesis) of cloud-water and cloud-ice grain mixing ratio those are variables related to cloud among NWP result data and the other method that used relative humidity. An experiment was carried out period from 23th to 24th July 2004. According to the sequence of comparing the derived cloud forecast data with the observed value, it was indicated that both of those have a practical use possibility as cloud forecast method. Specially in this Case study, cloud forecast method that use total cloud mixing ratio indicated good forecast availability to forecast of the low level clouds as well as middle and high level clouds.

• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.273-283
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• 2020

Quantitative forecasting of groundwater levels for the assessment of groundwater variation and vulnerability is very important. To achieve this purpose, various time series analysis and machine learning techniques have been used. In this study, we developed a prediction model based on LSTM (Long short term memory), one of the artificial neural network (ANN) algorithms, for predicting the daily groundwater level of 11 groundwater wells in Hankyung-myeon, Jeju Island. In general, the groundwater level in Jeju Island is highly autocorrelated with tides and reflected the effects of precipitation. In order to construct an input and output variables based on the characteristics of addressing data, the precipitation data of the corresponding period was added to the groundwater level data. The LSTM neural network was trained using the initial 365-day data showing the four seasons and the remaining data were used for verification to evaluate the fitness of the predictive model. The model was developed using Keras, a Python-based deep learning framework, and the NVIDIA CUDA architecture was implemented to enhance the learning speed. As a result of learning and verifying the groundwater level variation using the LSTM neural network, the coefficient of determination (R²) was 0.98 on average, indicating that the predictive model developed was very accurate.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.1
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• pp.27-38
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• 2012

This study is to analyse the hydrological behavior of agricultural reservoir using CAT (Catchment hydrologic cycle Assessment Tool). The CAT is a water cycle analysis model in order to quantitatively assess the characteristics of the short/long-term changes in watershed. It supports the effective design of water cycle improvement facilities by supplementing the strengths and weaknesses of existing conceptual parameter-based lumped hydrologic models and physical parameter-based distributed hydrologic models. The CAT especially supports the analysis of runoff processes in paddy fields and reservoirs. To evaluate the impact of agricultural reservoir operation and irrigation water supply on long-term rainfall-runoff process, the CAT was applied to Idong experimental catchment, operated for research on the rural catchment characteristics and accumulated long term data by hydrological observation equipments since 2000. From the results of the main control points, Idong, Yongdeok and Misan reservoirs, the daily water levels of those points are consistent well with observed water levels, and the Nash-Sutcliffe model efficiencies were 0.32~0.89 (2001~2007) and correlation coefficients were 0.73~0.98.

• Advances in environmental research
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• v.9 no.2
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• pp.123-133
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• 2020

A time series dataset was conducted to ascertain the effect of water table on the variability in and emission of CH₄ and CO₂ concentrations at a closed landfill site. An in-situ data of methane/carbon dioxide concentrations and environmental parameters were collected by means of an in-borehole gas monitor, the Gasclam (Ion Science, UK). Linear regression analysis was used to determine the strength of the correlation between ground-gas concentration and water table. The result shows CH₄ and CO₂ concentrations to be variable with strong negative correlations of approximately 0.5 each with water table over the entire monitoring period. The R² was slightly improved by considering their concentration over single periods of increasing and decreasing water table, single periods of increasing water table, and single periods of decreasing water table; their correlations increased significantly at 95% confidence level. The result revealed that fluctuations in groundwater level is the key driving force on the emission of and variability in groundgas concentration and neither barometric pressure nor temperature. This finding further validates the earlier finding that atmospheric pressure - the acclaimed major control on the variability/migration of CH₄ and CO₂ concentrations on contaminated sites, is not always so.

• Journal of Korea Water Resources Association
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• v.30 no.2
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• pp.131-141
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• 1997

In this study, the Storage Function Method and Loopnet Model (Unsteady flow analysis model) were used to construct the flood prediction system which can predict the effects of the water release in the downstream region of Teachong Dam. The regional frequency analysis (L-moment) was applied to compute frequency-based precipitation, and the flood prediction system was also used for flood routing of the down stream region of Teachong Dam in the Kum River Basin to calculate frequency based flood. The magnitude of flood, water level, discharge, and travel time to the major points of the downstream region of Teachong Dam, which can be used as an imdex of flood control management of Teachong Dam, were calculated.