• Journal of the Korean Society of Hazard Mitigation
• /
• v.10 no.2
• /
• pp.111-122
• /
• 2010

As the Jamsu-bridge and the floodplains of the Han River can be flooded during the rainy season, the exact prediction of the peak flood time is very important for mitigation of flood hazard. This study analyzes the effect of outflow of Paldang Dam and tide of Yellow Sea on the Han River. A target area is from the Paldang dam to Jeonryu gauging station. Water level of Jeonryu as a downstream boundary condition was estimated through multi linear regression analysis with outflow of Paldang dam and tide level of Incheon, because it was influenced by both a tide of Yellow Sea and outflow of Paldang dam. In this study, Water Level Rising Travel Time of the Jamsu-bridge and some floodplains in the Han River are estimated. Also, The second order polynomial expressions for relationships of outflow of Paldang Dam and Water Level Rising Travel Time were developed considering the outflow of Paldang dam and tide of Yellow Sea.

• Journal of Korean Society on Water Environment
• /
• v.39 no.1
• /
• pp.1-8
• /
• 2023

Monitoring and prediction of water quality are essential for effective river pollution prevention and water quality management. In this study, a multi-classification model was developed to predict chlorophyll-a (Chl-a) level in rivers. A model was developed using CatBoost, a novel ensemble machine learning algorithm. The model was developed using hourly field monitoring data collected from January 1 to December 31, 2015. For model development, chl-a was classified into class 1 (Chl-a≤10 ㎍/L), class 2 (10<Chl-a≤50 ㎍/L), and class 3 (Chl-a>50 ㎍/L), where the number of data used for the model training were 27,192, 11,031, and 511, respectively. The macro averages of precision, recall, and F1-score for the three classes were 0.58, 0.58, and 0.58, respectively, while the weighted averages were 0.89, 0.90, and 0.89, for precision, recall, and F1-score, respectively. The model showed relatively poor performance for class 3 where the number of observations was much smaller compared to the other two classes. The imbalance of data distribution among the three classes was resolved by using the synthetic minority over-sampling technique (SMOTE) algorithm, where the number of data used for model training was evenly distributed as 26,868 for each class. The model performance was improved with the macro averages of precision, rcall, and F1-score of the three classes as 0.58, 0.70, and 0.59, respectively, while the weighted averages were 0.88, 0.84, and 0.86 after SMOTE application.

• Journal of Korean Society on Water Environment
• /
• v.23 no.5
• /
• pp.591-599
• /
• 2007

The quality of water released from a stratified reservoir is dependent on various factors such as the location and shape of intake facility, structure of reservoir stratification, profile of water quality constituent, and withdrawal flux. Sometimes, selective withdrawal capabilities can provide the operational flexibility to meet the water quality demands both in-reservoir and downstream. The objective of this study was to evaluate the performance of a one-dimensional reservoir selective withdrawal model (SELECT) as a tool for supporting downstream water quality management for Daecheong and Imha reservoirs. The simulated water quality variables including water temperature, dissolved oxygen (DO), conductivity, turbidity were compared with the field data measured in tailwater. The model showed fairly satisfactory results and high reliability in simulating observations. The coefficients of determinant between simulated and observed turbidity values were 0.93 and 0.95 for Daecheong and Imha reservoirs, respectively. The outflow water quality was significantly influenced by water intake level under fully stratified condition, while the effect of intake amount was minor. In conclusion, the SELECT is simple but effective tool for supporting downstream water quality prediction and management for both reservoirs.

• Korean Journal of Agricultural Science
• /
• v.49 no.2
• /
• pp.193-202
• /
• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.

• Journal of Korea Water Resources Association
• /
• v.55 no.spc1
• /
• pp.1295-1303
• /
• 2022

As the role of water distribution networks (WDNs) becomes more important, identifying abnormal events (e.g., pipe burst) rapidly and accurately is required. Since existing approaches such as field equipment-based detection methods have several limitations, model-based methods (e.g., machine learning based detection model) that identify abnormal events using hydraulic simulation models have been developed. However, no previous work has examined the impact of data uncertainties on the results. Thus, this study compares the effects of measurement error-induced pressure data uncertainty in WDNs. An artificial neural network (ANN) is used to predict nodal pressures and measurement errors are generated by using cumulative density function inverse sampling method that follows Gaussian distribution. Total of nine conditions (3 input datasets × 3 output datasets) are considered in the ANN model to investigate the impact of measurement error size on the prediction results. The results have shown that higher data uncertainty decreased ANN model's prediction accuracy. Also, the measurement error of output data had more impact on the model performance than input data that for a same measurement error size on the input and output data, the prediction accuracy was 72.25% and 38.61%, respectively. Thus, to increase ANN models prediction performance, reducing the magnitude of measurement errors of the output pressure node is considered to be more important than input node.

• Journal of the Korean Geotechnical Society
• /
• v.15 no.5
• /
• pp.111-124
• /
• 1999

When deep excavation adjacent to an existing structure is performed, it is very important to minimize damage on the structure through the prediction of ground movement. In this paper, finite element analysis was performed to predict the ground movement, based on the data from site investigation and laboratory tests, when deep excavation close to a buried water tank was carried out in soft clay ground. The movement and stabilities of the soil-cement wall(SCW) and the adjacent structure were checked using the results of the analysis and the field measurement. The comparison between the measured and the predicted ground movements showed the significance of the excavation procedure and lowering of water level in the analytical model. In the future, it is needed to improve the prediction method for better estimation of the ground movement.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.16 no.4
• /
• pp.753-764
• /
• 1992

The steam generator water level is difficult to control at low power due to its reversed responses to the feedwater flow, which are well known as the shrink and swell phenomena. With regard to this problem a new control scheme has been studied by which the level transients could be kept within permissible ranges at low power. The relations between the various input conditions to steam generator and the level transients have been examined to be expressed in the form of process transfer functions. Analog filters have been incorporated to be expressed in the process with proper control constants. This control scheme allows the prediction of level variation together with the corresponding feedwater rate and results in mider transients with good stabilites.

• Journal of Korean Society of Disaster and Security
• /
• v.14 no.4
• /
• pp.17-27
• /
• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• Journal of Environmental Science International
• /
• v.26 no.9
• /
• pp.1031-1043
• /
• 2017

Recently severe drought caused the water shortage around the western parts of Chungcheongnamdo province, South Korea. A Diversion tunnel from the Geum river to the Boryong dam, which is the water supply dam for these areas has been proposed to solve this problem. This study examined hydraulic impacts on the Geum river associated with the diversion plan assuming the severe drought condition of 2015 would persist for the simulation period of 2016. The hydraulic simulation model was verified using hydrologic and hydraulic data including hourly discharges of the Geum river and its 8 tributaries, fluctuation of tidal level at the mouth of the river, withdrawals and return flows and operation records of the Geum river barrage since Feb. 1, 2015 through May 31, 2015. For the upstream boundary condition of the Geum river predicted inflow series using the nonlinear regression equation for 2015 discharge data was used. In order to estimate the effects of uncertainty in inflow prediction to the results total four inflow series consisting of upper limit flow, expected flow, lower limit flow and instream flow were used to examine hydraulic impacts of the diversion plan. The simulation showed that in cases of upper limit and expected flows there would be no problem in taking water from the Geum river mouth with a minimum water surface level of EL(+) 1.44 m. Meanwhile, the simulation also showed that in cases of lower limit flow and instream flow there would be some problems not only in taking water for water supply from the mouth of the Geum river but also operating the diversion facility itself with minimum water surface levels of EL(+) 0.94, 0.72, 0.43, and 0.14 m for the lower limit flow without/with diversion and the instream flow without/with diversion, respectively.

• Journal of KIISE
• /
• v.44 no.6
• /
• pp.607-612
• /
• 2017

This paper presents an attempt to apply Deep Learning technology to solve the problem of forecasting floods in urban areas. We employ Recurrent Neural Networks (RNNs), which are suitable for analyzing time series data, to learn observed data of river water and to predict the water level. To test the model, we use water observation data of a station in the Trinity river, Texas, the U.S., with data from 2013 to 2015 for training and data in 2016 for testing. Input of the neural networks is a 16-record-length sequence of 15-minute-interval time-series data, and output is the predicted value of the water level at the next 30 minutes and 60 minutes. In the experiment, we compare three Deep Learning models including standard RNN, RNN trained with Back Propagation Through Time (RNN-BPTT), and Long Short-Term Memory (LSTM). The prediction quality of LSTM can obtain Nash Efficiency exceeding 0.98, while the standard RNN and RNN-BPTT also provide very high accuracy.