• Title/Summary/Keyword: Real-time Rainfall

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Establishment and Application of Neuro-Fuzzy Real-Time Flood Forecasting Model by Linking Takagi-Sugeno Inference with Neural Network (I) : Selection of Optimal Input Data Combinations (Takagi-Sugeno 추론기법과 신경망을 연계한 뉴로-퍼지 홍수예측 모형의 구축 및 적용 (I) : 최적 입력자료 조합의 선정)

  • Choi, Seung-Yong;Kim, Byung-Hyun;Han, Kun-Yeun
    • Journal of Korea Water Resources Association
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    • v.44 no.7
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    • pp.523-536
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    • 2011
  • The objective of this study is to develop the data driven model for the flood forecasting that are improved the problems of the existing hydrological model for flood forecasting in medium and small streams. Neuro-Fuzzy flood forecasting model which linked the Takagi-Sugeno fuzzy inference theory with neural network, that can forecast flood only by using the rainfall and flood level and discharge data without using lots of physical data that are necessary in existing hydrological rainfall-runoff model is established. The accuracy of flood forecasting using this model is determined by temporal distribution and number of used rainfall and water level as input data. So first of all, the various combinations of input data were constructed by using rainfall and water level to select optimal input data combination for applying Neuro-Fuzzy flood forecasting model. The forecasting results of each combination are compared and optimal input data combination for real-time flood forecasting is determined.

A Study on the Reviesd Methods of Missing Rainfall Data for Real-time Forecasting Systems (실시간 예보 시스템을 위한 우량자료 보정 기법 연구)

  • Han, Myoung-Sun;Kim, Chung-Soo;Kim, Hyoung-Seop;Kim, Hwi-Rin
    • Journal of Korea Water Resources Association
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    • v.42 no.2
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    • pp.131-139
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    • 2009
  • The weather accidents by global warming effect are increasing rapidly whole world. Flood forcasting system and hydrological database are operated by almost all the countries in the world. An objective of this study is to research revised methods of missing rainfall data and find more effective revised method for this operating system. 194 rainfall data of the Han river basin is used. Arithmetic average method, coefficient of correlation weighting method and inverse distance weighting method are compared to estimate revised methods. The result from the analysis shows that coefficient of correlation weighting method is best quantitatively among the 3 methods.

Missing Hydrological Data Estimation using Neural Network and Real Time Data Reconciliation (신경망을 이용한 결측 수문자료 추정 및 실시간 자료 보정)

  • Oh, Jae-Woo;Park, Jin-Hyeog;Kim, Young-Kuk
    • Journal of Korea Water Resources Association
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    • v.41 no.10
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    • pp.1059-1065
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    • 2008
  • Rainfall data is the most basic input data to analyze the hydrological phenomena and can be missing due to various reasons. In this research, a neural network based model to estimate missing rainfall data as approximate values was developed for 12 rainfall stations in the Soyang river basin to improve existing methods. This approach using neural network has shown to be useful in many applications to deal with complicated natural phenomena and displayed better results compared to the popular offline estimating methods, such as RDS(Reciprocal Distance Squared) method and AMM(Arithmetic Mean Method). Additionally, we proposed automated data reconciliation systems composed of a neural network learning processer to be capable of real-time reconciliation to transmit reliable hydrological data online.

Establishment of flood forecasting and warning system in the un-gauged small and medium watershed through ODA (ODA사업을 통한 미계측 중소하천 유역 홍수예경보시스템 구축)

  • Koh, Deuk-Koo;Lee, Chihun;Jeon, Jeibok;Go, Sukhyon
    • Journal of Korea Water Resources Association
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    • v.54 no.6
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    • pp.381-393
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    • 2021
  • As part of the National Disaster Management Research Institute's Official Development Assistance (ODA) projects for transferring new technologies in the field of disaster-safety management, a flood forecasting and warning system was established in 2019 targeting the Borikhan in the Namxan River Basin in Bolikhamxai Province, Laos. In the target area, which is an ungauged small and medium river basin, observation stations for real-time monitoring of rainfall and runoff and alarm stations were installed, and a software that performs real-time data management and flood forecasting and warning functions was also developed. In order to establish a flood warning standard and develop a nomograph for flood prediction, hydraulic and hydrological analysis was performed based on the 30-year annual maximum daily rainfall data and river morphology survey results in the target area. This paper introduces the process and methodology used in this study, and presents the results of the system's applicability review based on the data observed and collected in 2020 after system installation.

Real Time Flood Forecasting Using Artificial Neural Networks (인공신경망 이론을 이용한 실시간 홍수량 예측 및 해석)

  • Kang, Moon-Seong;Park, Seung-Woo
    • 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).

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Real-time Forecasting of Daily Stream Flows (하천 일류출량의 실시간예측)

  • 정항우;이남호;박승우
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.32 no.3
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    • pp.47-55
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    • 1990
  • An adaptive algorithm was applied to forecast daily stream flows in real time using rainfall data. A three-component tank model was selected to simulate the flows and its time-variant parameters were self-calibrated with updated data using a parameter optimization scheme, golden section search method. The resulting adaptive model, APTANK, was applied to six watersheds, ranging from 0.47 to 33.62 km$^2$ size and the simulated daily streamflows were compared with the measured. The simulation results were in good agreement with the field data. APTANK is found to be applied to real-time flow simulation purposes such as a tool for irrigation water resources management and operations. The model is particularly good to simulate streamflows on dry days as compared to wet days having runoff-induced precipitation.

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Real-Time Flood Forecasting System For the Keum River Estuary Dam(I) -System Development- (금강하구둑 홍수예경보 시스템 개발(I) -시스템의 구성-)

  • 정하우;이남호;김현영;김성준
    • 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.

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The Comparative Analysis of Optimization Methods for the Parameter Calibration of Rainfall-Runoff Models (강우-유출모형의 매개변수 보정을 위한 최적화 기법의 비교분석)

  • Kim, Sun-Joo;Jee, Yong-Geun;Kim, Phil-Shik
    • Journal of The Korean Society of Agricultural Engineers
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    • v.47 no.3
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    • pp.3-13
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    • 2005
  • The conceptual rainfall-runoff models are used to predict complex hydrological effects of a basin. However, to obtain reliable results, there are some difficulties and problems in choosing optimum model, calibrating, and verifying the chosen model suitable for hydrological characteristics of the basin. In this study, Genetic Algorithm and SCE-UA method as global optimization methods were applied to compare the each optimization technique and to analyze the application for the rainfall-runoff models. Modified TANK model that is used to calculate outflow for watershed management and reservoir operation etc. was optimized as a long term rainfall-runoff model. And storage-function model that is used to predict real-time flood using historical data was optimized as a short term rainfall-runoff model. The optimized models were applied to simulate runoff on Pyeongchang-river watershed and Bocheong-stream watershed in 2001 and 2002. In the historical data study, the Genetic Algorithm and the SCE-UA method showed consistently good results considering statistical values compared with observed data.

Optimal Reservoir Operation Models for Paddy Rice Irrigation with Weather Forecasts (II) -Model Development- (기상예보를 고려한 관개용 저수지의 최적 조작 모형(II) -모형의 구성-)

  • 김병진;박승우
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.36 no.2
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    • pp.44-55
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    • 1994
  • This paper describes the development of real-time irrigation reservoir operation models that adequately allocate available water resources for paddy rice irrigation. Water requirement deficiency index(WRDI) was proposed as a guide to evaluate the operational performance of release schemes by comparing accumulated differences between daily release requirements for irrigated areas and actual release amounts. Seven reservoir release rules were developed, which are constant release rate method (CRR), mean storage curve method(MSC), frequency analysis method of reservoir storage rate(FAS), storage requirement curve method(SRC), constant optimal storage rate method (COS), ten-day optimal storage rate method(TOS), and release optimization method(ROM). Long-term forecasting reservoir operation model(LFROM) was formulated to find an optimal release scheme which minimizes WRDIs with long-term weather generation. Rainfall sequences, rainfall amount, and evaporation amount throughout the growing season were to be forecasted and the results used as an input for the model. And short-term forecasting reservoir operation model(SFROM) was developed to find an optimal release scheme which minimizes WRDIs with short-term weather forecasts. The model uses rainfall sequences forecasted by the weather service, and uses rainfall and evaporation amounts generated according to rainfall sequences.

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