• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.3
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• pp.60-66
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• 1994

This paper is to validate the proposed models for the real-time forecasting for the Keum river estuary dam such as tidal-level forecasting model, one-dimensional unsteady flood routing model, and Kalman filter models. The tidal-level forecasting model was based on semi-range and phase lag of four tidal constituents. The dynamic wave routing model was based on an implicit finite difference solution of the complete one-dimensional St. Venant equations of unsteady flow. The Kalman filter model was composed of a processing equation and adaptive filtering algorithm. The processng equations are second ordpr autoregressive model and autoregressive moving average model. Simulated results of the models were compared with field data and were reviewed.

• Journal of Environmental Science International
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• v.24 no.8
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• pp.1023-1036
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• 2015

A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.2
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• pp.45-57
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• 2003

An artificial neural network model was developed to analyze and forecast Short-term river runoff from the Naju watershed, in Korea. Error back propagation neural networks (EBPN) of hourly rainfall and runoff data were found to have a high performance In forecasting runoff. The number of hidden nodes were optimized using total error and Bayesian information criterion. Model forecasts are very accurate (i.e., relative error is less than 3% and $R^2$is greater than 0.99) for calibration and verification data sets. Increasing the time horizon for application data sets, thus mating the model suitable for flood forecasting. decreases the accuracy of the model. The resulting optimal EBPN models for forecasting hourly runoff consists of ten rainfall and four runoff data(ANN0410 model) and ten rainfall and ten runoff data(ANN1010 model). Performances of the ANN0410 and ANN1010 models remain satisfactory up to 6 hours (i.e., $R^2$is greater than 0.92).

• Water Engineering Research
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• v.5 no.4
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• pp.157-176
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• 2004

This research presents a prototype development and implementation of Decision Support System (DSS) for integrated river basin water management for the flood control. The DSS consists of Relational Database Management System, Hydrologic Data Monitoring System, Spatial Analysis Module, Spatial and Temporal Analysis for Rainfall Event Tool, Flood Forecasting Module, Real-Time Operation of Multi Reservoir System, and Dialog Module with Graphical User Interface and Graphic Display Systems. The developed DSS provides an automated process of alternative evaluation and selection within a flexible, fully integrated, interactive, centered relational database management system in a user-friendly computer environment. The river basin decision-maker for the flood control should expect that she or he could manage the flood events more effectively by fully grasping the hydrologic situation throughout the basin.

• Journal of Korea Water Resources Association
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• v.33 no.1
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• pp.87-98
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• 2000

The objective of this study is to test the flood forecasting capability of TOPMODEL on a single watershed in Korea. The selected study area is the Soyang River basin with outlet at Soyang Dam site. The three daily hydrographs and the three hourly flood events during 1990~1996 are selected for model calibrations and performance tests. The model parameters are estimated on 1990 daily event by manual fitting technique and the effects of topographic index distribution to river flow simulations are investigated on the study area. The model performance on correlation coefficient between the observed and the simulated flows for the verification periods are above 0.77 on the 95-, 96-daily events, while above 0.87 for 90-, 95-, 96-hourly events. By the consideration of flood flow characteristics in Korea, the physical interpretation of the model concept, and the model performance, it can be concluded that the TOPMODEL is feasible as a flood forecasting model in Korea. Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.363-363
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• 2023

Artificial Neural Networks (ANN) is a powerful tool for addressing various practical problems and it has been extensively applied in areas of water resources. In this study, Artificial Neural Networks (ANNs) were developed for flood forecasting at specific locations on the Han River. The Partial Mutual Information (PMI) technique was used to select input variables for ANNs that are neither over-specified nor under-specified while adequately describing the underlying input-output relationships. Historical observations including discharges at the Paldang Dam, flows from tributaries, water levels at the Paldang Bridge, Banpo Bridge, Hangang Bridge, and Junryu gauge station, and time derivatives of the observed water levels were considered as input candidates. Lagged variables from current time t to the previous five hours were assumed to be sufficient in this study. A three-layer neural network with one hidden layer was used and the neural network was optimized by selecting the optimal number of hidden neurons given the selected inputs. Given an ANN architecture, the weights and biases of the network were determined in the model training. The use of PMI-based input variable selection and optimized ANNs for different sites were proven to successfully predict water levels during flood periods.

• 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.53 no.3
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• pp.225-236
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• 2020

It is not easy to provide sufficient lead time for flood forecast in urban and small mountain basins using on-ground rain gauges, because the time concentration in those basins is too short. In urban and small mountain basins with a short lag-time between precipitation and following flood events, it is more important to secure forecast lead times by predicting rainfall amounts. The Han River Flood Control Office (HRFCO) in South Korea produces short-term rainfall forecasts using the Mcgill Algorithm for Precipitation-nowcast by Lagrangian Extrapolation (MAPLE) algorithm that converts radar reflectance of rainfall events. The Flash Flood Research Center (FFRC) in the Korea Institute of Civil Engineering and Building Technology (KICT) installed a flash flood forecasting system using the short-term rainfall forecast data produced by the HRFCO and has provided flash flood information in a local lvel with 1-hour lead time since 2019. In this study, we addressed the flash flood forecasting system based on the radar rainfall and the assessed the accuracy of the forecasting system for the recorded flood events occurred in 2019. A total of 31 flood disaster cases were used to evaluate the accuracy and the forecast accuracy was 90.3% based on the probability of detection.

• Water for future
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• v.25 no.3
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• pp.115-124
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• 1992

The peak flood discharge at a downstream station and the flood travel time between a pair of dams due to a specific flood release from the upper reservoir are computed using a hydraulic river channel routing method. The study covered the whole reservoir system in the Han River. The computed peak flood discharges and the travel times between dams were correlated with the duration and the magnitude of flood release rate at the upstream reservoir, and hence a multiple regression model is proposed for each river reach between a pair of dams. The peak flood discharge at a downstream location can be converted to the peak flood stage by rating curve. Hence, the proposed regression model could be used to forecast the peak flood stage at a downstream location and the flood travel time between dams using the information on the flood release rate and duration from the upper dam.

• Journal of Industrial Technology
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• v.18
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• pp.51-59
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• 1998

To flood forecastion, until now, Storage function method, Streamflow Synthesis and Reservoir Regulation, and HEC-1 model have been analysed generally in various definite simulation. Generally, Streamflow Synthesis and Reservoir Regulation and HEC-1 model are more delicacy and more excellent model than Storage function method in physically. But the resource huge for test of models. On the contrary, Storage function method has not only a few model various and data for decision but also has poor theory background in model excessively simpled water circulation about a basin. In this reason, this study is purpose to develop a statistical flood forecasting model that can forecast with accuracy variety of water height to Nak-Dong river vibration spots in flood with accumulated water resource.