• 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 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.

• Economic and Environmental Geology
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• v.56 no.1
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• pp.65-73
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• 2023

As one of the most vulnerable countries to floods, there should be an increased necessity for accurate and reliable flood forecasting in Indonesia. Therefore, a new prediction model using a machine learning algorithm is proposed to provide daily flood prediction in Indonesia. Data crawling was conducted to obtain daily rainfall, streamflow, land cover, and flood data from 2008 to 2021. The model was built using a Random Forest (RF) algorithm for classification to predict future floods by inputting three days of rainfall rate, forest ratio, and stream flow. The accuracy, specificity, precision, recall, and F1-score on the test dataset using the RF algorithm are approximately 94.93%, 68.24%, 94.34%, 99.97%, and 97.08%, respectively. Moreover, the AUC (Area Under the Curve) of the ROC (Receiver Operating Characteristics) curve results in 71%. The objective of this research is providing a model that predicts flood events accurately in Indonesian regions 3 months prior the day of flood. As a trial, we used the month of June 2022 and the model predicted the flood events accurately. The result of prediction is then published to the website as a warning system as a form of flood mitigation.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.694-698
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• 2005

본 연구에서는 우리나라 5대강 유역에 대한 홍수예경보시스템의 홍수추적방법으로 이용되고 있는 물리적인 모형인 저류함수법의 한계점을 극복하고, 영산강 유역의 본류를 대표하는 나주지점과 황룡강 유역을 대표하는 선암지점에 대하여 유역의 수문학적 구조를 나타내지 않는 인공신경망 이론을 이용하여 강우-유출 과정의 비선형 모형을 개발하였다. 또한, 신속한 홍수유출량 예측과 예측 결과에 따른 현장 적용이 가능하도록 CS(Client-Server) 기반에서 인공신경망에 대한 원시코드(source code)를 GUI(Graphical User Interface)화하여 홍수예측시스템(Flood Forecasting System : FFS)을 개발하였다. 본 연구결과 나주지점에서는 Model II의 ANN_NJ_9 모형이 선암지점에서는 Model III의 ANN_SA_9 모형이 강우-유출 특성을 가장 잘 반영하였다. 또한, 본 연구에서 개발한 GUI_FFS에 대하여 기 확보된 2004년도 강우 및 유출량 적용한 결과 0.98이상의 $R^2$값을 보임으로서 향후 수자원 및 하천계획 수립과 그에 따른 운영 및 관리에 효율성을 더할 수 있을 것이라 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.746-750
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• 2007

금강홍수통제소는 금강 유역의 홍수피해 경감을 목적으로 1990년 출범하였으며, 개소된 이래로 현재까지 홍수예보 및 수문 관측 업무를 수행하여 왔다. 금강홍수예보시스템의 유출모형은 이미 구축되어 있던 홍수예보 시스템과 마찬가지로 저류함수법과 단위도법에 의한 홍수 유출 모형을 근간으로 구성되어 있다. 최근 증설된 수문관측소를 반영하여 소유역을 재분할하고, 변화된 유역환경을 반영하여 저류함수모형에 대한 상수를 개선하고자 하였다. 소유역 및 하도분할과 티센계수 산정 등을 통해 저류함수법을 이용하기 위한 저류상수를 산정하기 위해 기존의 일반 종이지도로 제작된 지형도(1:50,000), 녹지자연도, 개략토양도 등을 이용하는 대신 수치지도를 이용하여 저류상수를 산정하였다. 새롭게 산정된 유역특성변수를 이용하여 유역의 저류상수를 산정하고 강우에 의한 유출량을 결정하였다. 변화된 유역 조건을 가지고 금강 유역의 전체 유역 및 하도유출계산을 수행한 후, 측정 결과가 있는 지점의 수문곡선과 비교하여 모형상수가 적절히 산정되었는지 검토하고, 개선된 모형상수를 제시하였다.

• Journal of Korea Water Resources Association
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• v.33 no.6
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• pp.689-699
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• 2000

This paper presents the integrated storage function model (ISFM) to improve the accuracy of the storage function model (SFM) which is widely employed for flood runoff analysis and its forecasting in Korea. In order to achieve this objective, the optimization method is applied for estimation of parameters of the model which dominate the accuracy of the analysis, which is usually taken by empirical formulae, and they are treated as time dependent variables. The fuzzy control technique is used to detennine the time variant parameters. In addition, the ISFM can be applied to the combined routing of the watershed and the channel with a residual watershed.ershed.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1783-1787
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• 2007

In this paper we applied radar rainfall for assessment that radar can be used for flood forecasting. The radar data observed at Imjin-River radar site was adjusted using conditional merging method to estimate simulated runoff in Anseon-cheon basin. Also we use two dimensional physical and grid based model call $Vflo^{TM}$. As a result we could find simulated hydrologic curve shows good fitting with observed hydrologic curve even parameters of the model were not calibrated. If we calibrate the parameters, we can expect better hydrologic curve. And radar rainfall can be used for water resources fields and flood forecasting in Korea.

• Water for future
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• v.29 no.1
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• pp.151-161
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• 1996

The proposed flood forecasting system combines a flood routing model with a parameter estimation model. In the parameter estimation model system states and parameters are treated with the extended state-space formulation. The extended Kalman filter is adopted to estimate the states and parameters. A sensitivity analysis is used to investigate the relative significance of the parameters. Insensitive parameters are treated as constants and parameters that are mutually correlated are combined in a simplified form. The developed estimation methodology is applied todam sites of the multi-purpose reservoirs in Korea. The forecasted hydrographs from the extended Kalman filter satisfactorily coincide with the observed. From the time sequence plots of estimated parameters, it is found that the storage coefficient is almost constant, but exponent varies appreciably in time.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.371-371
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• 2020

도시 및 소규모 산지 유역에서와 같이 지체시간이 짧은 유역에서 발생하는 돌발홍수는 더 이상 우량계만으로 예보가 불가능하다. 그리고 지역에 따라 침수시간이나 침수심이 달라지기 때문에 지역에 따른 침수특성과 유속특성의 관계식을 산정하여 홍수예보 기준을 설정하였다. 더불어 도달시간이 짧은 도시 및 산지에서는 지체시간 외에 강수 예측을 통한 홍수예보 선행시간을 확보하는 것이 매우 중요하다. 본 연구에서는 한강홍수통제소의 강우레이더 기반 초단기 외삽 예측을 입력자료로 활용하여 돌발홍수 예측 시스템을 구축하였다. 강우레이더 기반 초단기 외삽 예측은 강우강도를 입력으로 사용하기 때문에 예측에 별도의 정량 보정이 필요하지 않다는 장점이 있다. 2019년도에 발생한 다양한 홍수 사고 사례를 분석하여 본 시스템에 대한 정확도를 평가하였다. 본 시스템은 동(읍/면) 단위로 1시간 선행 예보를 3단계 위험 정보(주의/경계/심각)로 제공할 수 있다.

• 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.