• Journal of Korea Water Resources Association
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• v.48 no.5
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• pp.409-423
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• 2015

In this study the very short-term rainfall forecasting and storm water forecasting using the weather radar data were implemented in an urban stream basin. As forecasting time increasing, the very short-term rainfall forecasting results show that the correlation coefficient was decreased and the root mean square error was increased and then the forecasting model accuracy was decreased. However, as a result of the correlation coefficient up to 60-minute forecasting time is maintained 0.5 or higher was obtained. As a result of storm water forecasting in an urban area, the reduction in peak flow and outflow volume with increasing forecasting time occurs, the peak time was analyzed that relatively matched. In the application of storm water forecasting by radar rainfall forecast, the errors has occurred that we determined some of the external factors. In the future, we believed to be necessary to perform that the continuous algorithm improvement such as simulation of rapid generation and disappearance phenomenon by precipitation echo, the improvement of extreme rainfall forecasting in urban areas, and the rainfall-runoff model parameter optimizations. The results of this study, not only urban stream basin, but also we obtained the observed data, and expand the real-time flood alarm system over the ungaged basins. In addition, it is possible to take advantage of development of as multi-sensor based very short-term rainfall forecasting technology.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.280.2-286
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• 1998

Flood is the main natural disaster mostly in the world. It is a care problem to prevent flood disaster generally. The frequency of flood disaster is high and the distributing field is wide, the 50 percent population and 70 percent properties distribute at the threaten field of flood disaster in China. Flood disaster has caused a huge amount of economical losses and these losses have an increasing trend. Along with the development of reducing natural disaster action, it has become one of the most attentive problems for monitoring flood, preventing flood and forecasting flood efficiently. Remote sensing has the characteristics of large spatial observing areas, wide spectrum ranges, and imaging far away from the targets, imaging capabilities all weather. Spatial remote sensing information, which records the full, processes of the disaster's occurrence and development in real-time. It is a scientific basis for management, planning and decision-making. Through systemic analyzing the RS monitoring theory, based on compounding RS information, the technology and method of monitoring flood disaster are studied.

• Water for future
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• v.25 no.4
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• pp.109-119
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• 1992

The purpose of this study is to develop real-time streamflow forecasting models in order to manage effectively the flood warning system and water resources during the storm. The stochastic system models of the rainfall-runoff process using in this study are constituted and applied the Recursive Least Square and the Instrumental Variable-Approximate Maximum Likelihood algorithm which can estimate recursively the optimal parameters of the model. Also, in order to improve the performance of streamflow forecasting, initial values of the model parameter and covariance matrix of parameter estimate errors were evaluated by using the observed historical data of the hourly rainfall-runoff, and the accuracy and applicability of the models developed in this study were examined by the analysis of the I-step ahead streamflow forecasts.

• Journal of Korea Water Resources Association
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• v.43 no.2
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• pp.221-231
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• 2010

A regression water level forecasting model using data from stage and rainfall monitoring stations is developed to solve the difficulties which real-time forecasting models could not get the reliabilities by assuming future rainfall duration and intensity. The model could forecast future water levels of maximum 2 hours after using data from monitoring stations in Daejeon area. It shows stable forecasts by its maximum standard deviation is 5 cm, average standard deviations are 1~4 cm and most of coefficients of determination are larger than 0.95. It shows also more researches about the stationary of watershed which assumed in this regression method are necessary.

• Journal of Korea Water Resources Association
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• v.30 no.5
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• pp.449-457
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• 1997

This study attempts to develop a stochastic-dynamic real-time flow forecasting model for an event-orient watershed storage function model (SFM), which has been used as an official flood computation model in Korea, and to evaluate its performance for real-time flow forecast. The study area is the 747.5$\textrm{km}^2$ Hwecheon basin with outlet at Gaejin and the 8 single flow events during 1983-1986 are selected for comparison and verification of model parameter and model performance. The used model parameters in this study are the same values on field work. It is shown that results from the existing model highly depend on the events, but those from the developed model are stable and well predict the flows for the selected flood events. The coefficient of model efficiency between observed and predicted flows for the events was above 0.90. It is concluded that the developed model that can consider model and observation uncertainties during a flood event is feasible and produces reliable real-time flow forecasts on the area.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.259-259
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• 2016

This study examined Artificial Neurons Networks model (ANNs) for forecast flash discharge at Southern part of Thailand by using rainfall data and discharge data. The Sungai Kolok River Basin has meant the border crossing between Thailand and Malaysia which watershed drains an area lies in Thailand 691.88 square kilometer from over all 2,175 square kilometer. The river originates in mountainous area of Waeng district then flow through Gulf of Thailand at Narathiwat Province, which the river length is approximately 103 kilometers. Almost every year, flooding seems to have increased in frequency and magnitude which is highly non-linear and complicated phenomena. The purpose of this study is to forecast runoff on Sungai Kolok at X.119A gauge station (Sungai Kolok district, Narathiwat province) for 3 days in advance by using Artificial Neural Networks model (ANNs). 3 daily rainfall stations and 2 daily runoff station have been measured by Royal Irrigation Department and Meteorological Department during flood period 2000-2014 were used as input data. In order to check an accuracy of forecasting, forecasted runoff were compared with observed data by pursuing Coefficient of determination ($R^2$). The result of the first day gets the highest accuracy and then decreased in day 2 and day 3, consequently. $R^2$values for first day, second day and third day of runoff forecasting is 0.71, 0.62 and 0.49 respectively. The results confirmed that the ANNs model can be used when the range of collected dataset is short and real-time operated. In conclusion, the ANNs model is suitable to runoff forecasting during flood incident of Sungai Kolok river because it is straightforward model and require with only a few parameters for simulation.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.711-713
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• 2005

• Ecology and Resilient Infrastructure
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• v.11 no.1
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• pp.23-23
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• 2024

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.12-31
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• 2010

The objective of this study is to evaluate the applicability of GIS based multi-directional flow allocation model. In order to evaluate the suggested model in this study, it was applied to real watersheds, Pyeongchang and Soyang river basin. The simulation results were compared with observed values, and showed good agreements. The improvement of accuracy and reduction of simulation time were carried out by applying multi-directional flow allocation. Accordingly, the applied methodologies presented in this study will be used to predict accurate runoff, which plays a major role in integrated flood management. If this model is combined with the techniques of rainfall forecasting, it will contribute to the real-time flood forecasting and warning in the future.