• Proceedings of the Korea Contents Association Conference
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• 2016.05a
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• pp.253-254
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• 2016

• Journal of Soil and Groundwater Environment
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• v.12 no.4
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• pp.54-59
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• 2007

In this study, a semi-analytical model to address groundwater level fluctuations in response to precipitations and its infiltration is developed through mathematical modeling based on water balance equation. The developed model is applied to a prediction of groundwater level fluctuations in Hongcheon area. The developed model is calibrated through a nonlinear parameter estimator by using daily precipitation rates and groundwater fluctuations data of a same year 2003. The calibrated input parameters are directly applied to the prediction of groundwater fluctuations of year 2004 and the simulated curve successfully mimics the observed. The developed model is also applied to practical problems such as a prediction of a effect of reduced recharge due to surface coverage change and a induced water level reduction. Through this study, we found that recharge to precipitation ratio is not a constant and may be a function of a precipitation pattern.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.457-467
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• 2010

The settlement prediction is very important to preloading method for a construction site on a soft ground. At the design stage, however, it is hard to predict the settlement exactly due to limitations of the site survey. Most of the settlement prediction is performed by a regression settlement curve based on the field data during a construction. In Korea, hyperbolic method has been most commonly used to align the settlement curve with the field data, because of its simplicity and many application cases. The results from hyperbolic method, however, may be differed by data selections or data fitting methods. In this study, the analyses using hyperbolic method were performed about the field data of $\bigcirc\bigcirc$ site in Pusan. Two data fitting methods, using an axis transformation or an alternative method, were applied with the various data group. If data was used only after the ground water level being stabilized, fitting results using both methods were in good agreement with the measured data. Without the information about the ground water level, the alternative method gives better results with the field data than the method using an axis transformation.

• Journal of Korean Society on Water Environment
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• v.22 no.5
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• pp.952-957
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• 2006

The present study applied wavelet transform and artificial neural networks (ANNs) for the prediction of daily TOC data. TOC data were transformed into denoised data by the wavelet transform and the noise-reduced data were used for the prediction model by artificial neural networks. For the application of wavelet transform, Daubechies wavelet of order 10 ('db10') was used as a basis function and decomposed the TOC data up to fifth level with five detail components and one approximation component. ANNs were calibrated with the input data of the segregated TOC data corresponding to the details from second to fifth level and the approximation. Consequently, the ANNs model for the prediction of daily TOC data showed the best result when it had seventeen hidden nodes in its layer.

• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.5
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• pp.75-86
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• 2005

Recently since urbanization has brought about a dried stream and a worse water quality, Anyang city discharged the third treated sewage into the upper stream of Anyancheon and Hakuicheon. As the result, Hakuicheon had the water level and velocity enough for a living thing in the water to live in but water quality was worse than it had been. Therefore in case of meeting the water level and velocity of the second grade water-quality which living things in the water can live in, the discharge and water quality to secure in-stream flow must be at least 0.350 $m^3/s$ and $BOD_5\;3.2 mg/{\iota}$ respectively. In Anyancheon the water level was increased a little higher than it had been but the velocity was almost unchanged in comparison with it before. On the other hand the water quality was a little better than it had been. Therefore in case of meeting the water level and velocity of the third grade water-quality that people can do water-friendly activity, the discharge and water quality to secure in-stream flow must be at least 0.688 $m^3/s$ and $BOD_5\;4.8 mg/{\iota}$ respectively. The water-quality prediction on the suggested eight scenarios was simulated in all satisfying the third grade water-quality.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.874-881
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• 2022

Until 2016, before discussions on the restoration of brackish water of the Nakdong River Estuary started in earnest, the downstream water level was predicted using the data of existing tide level observatories (Busan and Gadeokdo) several kilometers away from the estuary. However, it was not easy to carry out the prediction due to the dif erence in tide level and phase. Therefore, this study was conducted to estimate tide prediction more accurately through tidal harmonic analysis using the measured water level affected by the tides in the offshore waters adjacent to the Nakdong River Estuary. As a research method, the storage status of observation data according to the period and abnormal data were checked at 10-minute intervals in the offshore sea area near the Nakdong River Estuary bank, and the observed and predicted tides were measured using TASK2000 (Tidal Analysis Software Kit) Package, a tidal harmonic analysis program. Regression analysis based on one-to-one comparison showed that the correlation between the two components was high correlation coef icient 0.9334. In predicting the tides for the current year, if possible, more accurate data can be obtained by harmonically analyzing one-year tide observation data from the previous year and performing tide prediction using the obtained harmonic constant. Based on this method, the predicted tide for 2022 was generated and it is being used in the calculation of seawater inflow for the restoration of brackish water of the Nakdong River Estuary.

• Water for future
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• v.24 no.3
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• pp.83-94
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• 1991

In this paper a discrete extended Kalman filter for the tidal prediction has been developed. The filter is based on a set of difference equations derived from the one dimensional shallow water equations using the finite difference scheme proposed by Lax-Wendroff. The filter gives estimates of the water level and water velocity, together with the parameters in the model which essentially have a random character, e.g. bottom friction and wind stress. The estimates are propagated and updated by the filter when the physical circumstances change. The Kalman-filter is applied to field data gathered in the coastal area alon the West Sea and it is shown that the filter gives satisfactory results in forecasting the waterlevels during storm surge periods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.216-218
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• 2022

Natural disasters caused by abnormal climates are continuously increasing, and the types of natural disasters that cause the most damage are flood damage caused by heavy rains and typhoons. Therefore, in order to reduce flood damage, this paper proposes a system that can predict the water level, a major parameter of flood, in real time using LSTM and GRU. The input data used for flood prediction are upstream and downstream water levels, temperature, humidity, and precipitation, and real-time prediction is performed through the pre-trained LSTM-GRU model. The input data uses data from the past 20 hours to predict the water level for the next 3 hours. Through the system proposed in this paper, if the risk determination function can be added and an evacuation order can be issued to the people exposed to the flood, it is thought that a lot of damage caused by the flood can be reduced.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.246-246
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• 2023

Multi-purpose dams are operated accounting for both physical and socioeconomic factors. This study aims to evaluate the utility of a deep learning algorithm-based model for three multi-purpose dam operation (Seomjin River dam, Juam dam, and Juam Control dam) in Seomjin River. In this study, the Gated Recurrent Unit (GRU) algorithm is applied to predict hourly water level of the dam reservoirs over 2002-2021. The hyper-parameters are optimized by the Bayesian optimization algorithm to enhance the prediction skill of the GRU model. The GRU models are set by the following cases: single dam input - single dam output (S-S), multi-dam input - single dam output (M-S), and multi-dam input - multi-dam output (M-M). Results show that the S-S cases with the local dam information have the highest accuracy above 0.8 of NSE. Results from the M-S and M-M model cases confirm that upstream dam information can bring important information for downstream dam operation prediction. The S-S models are simulated with altered outflows (-40% to +40%) to generate the simulated water level of the dam reservoir as alternative dam operational scenarios. The alternative S-S model simulations show physically inconsistent results, indicating that our deep learning algorithm-based model is not explainable for multi-purpose dam operation patterns. To better understand this limitation, we further analyze the relationship between observed water level and outflow of each dam. Results show that complexity in outflow-water level relationship causes the limited predictability of the GRU algorithm-based model. This study highlights the importance of socioeconomic factors from hidden multi-purpose dam operation processes on not only physical processes-based modeling but also aritificial intelligence modeling.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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• pp.133-139
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• 2023

This paper proposes a two-stage Water Shield Automation System (WSAS) to predict the possibility of river overflow and vehicle flooding due to sudden rainfall. The WSAS uses a two-stage Deep Neural Network (DNN) model. First, a river overflow prediction module is designed with LSTM to decide whether the river is flooded by predicting the river's water level rise. Second, a vehicle flooding prediction module predicts flooding of underground parking lots by detecting flooded tires with YOLOv5 from CCTV images. Finally, the WSAS automatically installs the water barrier whenever the river overflow and vehicle flooding events happen in the underground parking lots. The only constraint to implementing is that collecting training data for flooded vehicle tires is challenging. This paper exploits the Image C&S data augmentation technique to synthesize flooded tire images. Experimental results validate the superiority of WSAS by showing that the river overflow prediction module can reduce RMSE by three times compared with the previous method, and the vehicle flooding detection module can increase mAP by 20% compared with the naive detection method, respectively.