• Journal of Soil and Groundwater Environment
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• v.20 no.3
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• pp.74-82
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• 2015

A method to filter out the effect of river stage fluctuations on groundwater level was designed using an artificial neural network-based time series model of groundwater level prediction. The designed method was applied to daily groundwater level data near the Gangjeong-Koryeong Barrage in the Nakdong river. Direct prediction time series models were successfully developed for both cases of before and after the barrage construction using past measurement data of rainfall, river stage, and groundwater level as inputs. The correlation coefficient values between observed and predicted data were over 0.97. Using the time series models the effect of river stage on groundwater level data was filtered out by setting a constant value for river stage inputs. The filtered data were applied to the hybrid water table fluctuation method in order to estimate the groundwater recharge. The calculated ratios of groundwater recharge to precipitation before and after the barrage construction were 11.0% and 4.3%, respectively. It is expected that the proposed method can be a useful tool for groundwater level prediction and recharge estimation in the riverside area.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.291-291
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• 2011

우리나라는 기상학적 지리학적 영향으로 여름철에 강우가 집중하여 내리며 최근에는 짧은 시간에 많은 양의 강우가 내리는 집중호우의 발생빈도가 증가하고 있다. 이러한 집중호우는 하천의 수위를 증가시켜 하천범람 및 제방붕괴의 위험을 가져와 많은 재산과 인명피해를 가져올 수 있다. 하천 수위의 예측은 기존에 물리적, 개념적 모형을 통해 강우-유출을 해석하는 과정에서 주로 다루어 졌다. 그러나 자연현상인 강우와 유출관계를 규명하는 과정은 지역의 다양한 특성, 강우의 시 공간적 분포 등 복잡하고 다양한 인자를 고려해야 한다는 문제와 부딪쳐 많은 어려움을 겪어왔다. 따라서 본 연구에서는 복잡한 비선형 과정들의 모형화가 가능한 인공 신경망 모형을 이용하여 수위예측 모형을 구성하고 100mm이상의 강우가 연속해서 내린 호우사상을 훈련시켜 집중호우 발생시 수위예측에 활용하고자 하였다. 이를 위해 구성된 인공신경망 모형을 금강유역 보청천에 적용한 결과 중소하천유역인 보청천 유역의 홍수위 예측에 적용이 가능함을 확인하였다.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.5
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• pp.572-581
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• 2005

This research is conducted to develop predictable method of real scale nanofiltration treatability with small scale nanofiltration experiments. As a result of comparing calculated values with measured values, they are in a good agreement for the concentrations in filtered water and concentrated water. The results of that are not affected by change of system recovery from 20% to 95%. The proposed method is produced using constant recovery of elements, that is, no considering the pressure change. we can predict filtrated flux and contaminant concentrations with the method. The method has the following steps. (1) Calculate recovery of each element with water quality level after fixing recovery elements, (2) Predict system recovery with recovery of elements in 1, 2, 3, and 4 banks, (3) Run small scale nanofiltration experiments in predicted water quality and (4) Simulate large scale nanofiltration system for forecasting actual water quality. As the cost for nanofiltration pretest will reduced if we use the proposed method, it will be a promising method for introducing nanofiltration to supply safe drinking water.

• Journal of Korea Water Resources Association
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• v.33 no.3
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• pp.331-340
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• 2000

This study aims to develop a methodology of real time forecasting of mundation risk based on DAMBRK model and Kalman filter. The model is based on implicit, nonlinear finite difference approximatIons of the one-dimensional dynamic wave equations. The stochastic estimator uses on extended Kalman filter to provide optimal updating estimates. These are accomplished by combining the predictions of the determurustic model with real time observauons modified by the Kalman filter gain ractor. Inundation risks are also estimated by applying Monte Carlo simulation to consider the variability in cross section geometry and Manning's roughness coefficient. The model calibrated by applying to the floods ot South Han River on September, 1990 and August, 1995. The Kalman tilter model indicates that significant improvement compared to deteriministic analysis in flood routing predictions in the river. Overtopping risk of levee is also presented by comparing levee height with simulated flood level. level.

• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.1
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• pp.27-38
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• 2006

In this study, we developed a program to estimate discharge efficiently considering major hydraulic characteristic including water level, river bed, water slope and roughness coefficient in a natural river. Stream discharge was measured at Gongju gauge station located in the down stream of the Daechung Dam during normal and dry seasons from 2003 to 2004. The developed model was compared with the results from the existing rating curve at T/M gage stations, and was used for runoff analyses. Evaluating the developed river discharge estimation program, it was applied during 1983-2004 that base flow separation method and RRFS (Rainfall Runoff Forecasting System) which is based on SSARR (Streamflow Synthesis And Resevoir Regulation). The result presents the stage-discharge curve creator range at the Gong-ju is overestimated by approximately $10-20\%$, especially at the low stage. It is attributed to the hydraulic characteristics at the study. The discharge simulated by the RRFS and base flow separation, which is calibrated using the measurement at the early spring and late fall season during relatively d]v season, shows the least errors. The coefficient of roughness at Gongju station varied with the high and low water level.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.15-25
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• 2012

This study is to develop a web-based real-time agricultural flood management system(RAFMS) for 378 agricultural reservoirs equipped with auto water level gauge stations. The RAFMS was designed to operate linking with Rural Agricultural Water Resource Information System(RAWRIS) which supports data viz. real-time rainfall and water level necessary for RAFMS. The system was constituted to monitor the floods simultaneously at each reservoir by calculating the real-time reservoir inflow from watersheds, water level, and release to downstream. In addition, the system has the prediction function for the flood by applying weather forecasting data from Korea Meteorological Administration(KMA).

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1207-1216
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• 2018

Discharge or water level predictions at tidally affected river reaches are currently still a great challenge in hydrological practices. This research aims to predict water level of the tide dominated site, Jamsu bridge in the Han River downstream. Physics-based hydrodynamic approaches are sometimes not applicable for water level prediction in such a tidal river due to uncertainty sources like rainfall forecasting data. In this study, TensorFlow deep learning framework was used to build a deep neural network based LSTM model and its applications. The LSTM model was trained based on 3 data sets having 10-min temporal resolution: Paldang dam release, Jamsu bridge water level, predicted tidal level for 6 years (2011~2016) and then predict the water level time series given the six lead times: 1, 3, 6, 9, 12, 24 hours. The optimal hyper-parameters of LSTM model were set up as follows: 6 hidden layers number, 0.01 learning rate, 3000 iterations. In addition, we changed the key parameter of LSTM model, sequence length, ranging from 1 to 6 hours to test its affect to prediction results. The LSTM model with the 1 hr sequence length led to the best performing prediction results for the all cases. In particular, it resulted in very accurate prediction: RMSE (0.065 cm) and NSE (0.99) for the 1 hr lead time prediction case. However, as the lead time became longer, the RMSE increased from 0.08 m (1 hr lead time) to 0.28 m (24 hrs lead time) and the NSE decreased from 0.99 (1 hr lead time) to 0.74 (24 hrs lead time), respectively.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.981-992
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• 2023

Due to recent global climate change, the scale of flood damage is increasing as rainfall is concentrated and its intensity increases. Rain on a scale that has not been observed in the past may fall, and long-term rainy seasons that have not been recorded may occur. These damages are also concentrated in ASEAN countries, and many people in ASEAN countries are affected, along with frequent occurrences of flooding due to typhoons and torrential rains. In particular, the Bandung region which is located in the Upper Chitarum River basin in Indonesia has topographical characteristics in the form of a basin, making it very vulnerable to flooding. Accordingly, through the Official Development Assistance (ODA), a flood forecasting and warning system was established for the Upper Citarium River basin in 2017 and is currently in operation. Nevertheless, the Upper Citarium River basin is still exposed to the risk of human and property damage in the event of a flood, so efforts to reduce damage through fast and accurate flood forecasting are continuously needed. Therefore, in this study an artificial intelligence-based river flood water level forecasting model for Dayeu Kolot as a target station was developed by using 10-minute hydrological data from 4 rainfall stations and 1 water level station. Using 10-minute hydrological observation data from 6 stations from January 2017 to January 2021, learning, verification, and testing were performed for lead time such as 0.5, 1, 2, 3, 4, 5 and 6 hour and LSTM was applied as an artificial intelligence algorithm. As a result of the study, good results were shown in model fit and error for all lead times, and as a result of reviewing the prediction accuracy according to the learning dataset conditions, it is expected to be used to build an efficient artificial intelligence-based model as it secures prediction accuracy similar to that of using all observation stations even when there are few reference stations.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.287-287
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• 2023

Surface water-groundwater interaction (SWGI) is an important hydrological process that influences both the quantity and quality of water resources. However, regional scale SWGI model calibration and uncertainty analysis have been a challenge because integrated models inherently carry a vast number of parameters, modeling assumptions, and inputs, potentially leaving little time and budget to explore questions related to model performance and forecasting. In this study, we have proposed the application of iterative ensemble smoother (IES) for uncertainty analysis and calibration of the widely used integrated surface-subsurface model, SWAT-MODFLOW. SWAT-MODFLOW integrates Soil and Water Assessment Tool (SWAT) and a three-dimensional finite difference model (MODFLOW). The model was calibrated using a parameter estimation tool (PEST). The major advantage of the employed IES is that the number of model runs required for the calibration of an ensemble is independent of the number of adjustable parameters. The pilot point approach was followed to calibrate the aquifer parameters, namely hydraulic conductivity, specific storage, and specific yield. The parameter estimation process for the SWAT model focused primarily on surface-related parameters. The uncertainties both in the streamflow and groundwater level were assessed. The work presented provides valuable insights for future endeavors in coupled surface-subsurface modeling, data collection, model development, and informed decision-making.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.