• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2017.11a
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• pp.56-57
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• 2017

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.277-280
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• 2002

An artificial neural network model was developed to analyze and forecast real time river runoff from the Naju watershed, in Korea. Model forecasts are very accurate (i.e., relative error is less than 3% and $R^2$ is great than 0.99) for calibration data sets. Increasing the time horizon for validation data sets, thus making the model suitable for flood forecasting, decreases the accuracy of the model. The resulting optimal EBPN models for forecasting real time runoff consists of ten rainfall and four and ten runoff data (ANN0410 and ANN1010 models). Performances of the ANN0410 and ANN1010 models remain satisfactory up to 6 hours (i.e., $R^2$ is great than 0.92).

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.285-288
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• 2002

For real-time flood forecasting and effective control flood at the Youngsan estuary dam, the Flood Forecasting and Control User Interface System II (FFCUS II) has been developed. This paper describes the features and application of FFCUS II. FFCUS II is composed of the database management subsystem, the model subsystem, and the graphic user interface. The database management subsyem collects rainfall data and stream flow data, updates, processes, and searches the data. The model subsystem predicts the inflow hydrograph, the tide, forecasts flood hydrograph, and simulates the release rate from the sluice gates. The graphic user interface subsystem aids the user's decision-making process by displaying the operation results of the database management subsystem and model subsystem.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.2
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• pp.85-93
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• 2001

The basic concept of the model is to minimize the error range between forecasted flood inflow and actual flood inflow, and forecast accurately the flood discharge some hours in advance depending on the concentration time(Tc) and soil moisture retention storage(Sa). Simplex method that is a multi-level optimization technique was used to search for the determination of the best parameters of RETFLO (REal-Time FLOod forecasting) model. The flood forecasting model developed was applied to several strom event of Yedang reservoir during past 10 years. Model perfomance was very good with relative errors of 10% for comparison of total runoff volume and with one hour delayed peak time.

• Magazine of the Korean Society of Agricultural Engineers
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• v.34 no.1
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• pp.100-106
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• 1992

The objective of this study is to develop a real-time runoff forecasting model considering stochastic component. The model is composed of deterministic and stochastic components. Simplified tank model was selected as a deterministic runoff forecasting model. The time series of estimation residual resulting from the tank model simulation was analyzed and was best suited to the second-order autoregressive model. ARTANK model which combined the tank model with the autoregressive process was developed. And it was applied to a BANWEOL basin for validation. The simulation results showed a good agreement with the observed field data.

• Magazine of the Korean Society of Agricultural Engineers
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• v.37 no.2
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• pp.13.2-20
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• 1995

An water balance model was formulated to simulate the change in water levels at the estuary reservoir from sluice gate releases and the inflow hydrographs, and an one-di- mensional flood routing model was formulated to simulate temporal and spatial varia- tions of flood hydrographs along the estuarine river. Flow rates through sluice gates were calibrated with data from the estuary dam, and the results were used for a water balance model, which did a good job in predicting the water level fluctuations. The flood routing model which used the results from two hydrologic models and the water balance model simulated hydrographs that were in close agreement with the observed data. The flood forecasting model was found to be applicable to real-time forecasting of water level fluc- tuations with reasonable accuracies.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.2
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• pp.79-87
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• 1994

A real-time flood forecasting system(FLOFS) was developed for the real-time and predictive determination of flood discharges and stages, and to aid in flood management decisions in the Keum River Estuary Dam. The system consists of three subsystems : data subsystem, model subsystem, and user subsystem. The data subsystem controls and manages data transmitted from telemetering systems and simulated by models. The model subsystem combines various techniques for rainfall-runoff modeling, tidal-level forecasting modeling, one-dimensional unsteady flood routing, Kalman filtering, and autoregressivemovingaverage(ARMA) modeling. The user subsystem in a menu-driven and man-machine interface system.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.3
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• pp.67-73
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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 The Korean Society of Agricultural Engineers
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• v.58 no.6
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• pp.71-77
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• 2016

This study was to evaluate the feasibility of pre-consolidation pressure distribution characteristic of western and southern coastal region, using correlation of unconfined compressive strength and preceding research equation. Pre-consolidation of western and southern region showed similar trends undrained shear strength and pre-consolidation pressure in proportion to unconfined compressive strength. Predicted results of U.S. NAVY. (1982) equation revealed a small error western 9.7 % and southern 0.4 %. Prediction correlation results of pre-consolidation using unconfined compressive strength revealed an error western 16.8 % and southern 0.7 %. It was reported that less than 20 percent of pre-consolidation pressure prediction result of Casagrande forecasting error. Estimates of pre-consolidation pressure are possible, before the standard consolidation test, because it was reported that less than 20 % of the forecasting errors of Casagrande.

• Journal of Biosystems Engineering
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• v.17 no.2
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• pp.177-191
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• 1992

The MAFSAP(Monitoring and Forecasting System of Air Pollution) was developed to measure the weather and air pollution data automatically, then make them input to microcomputer and analyze them for monitoring and forecasting air pollution at all times. And the air pollution telemetering systems installed at Young-Dong Thermal Power Plant was analyzed and an ideal telemetering system utilizing MAFSAP was suggested.