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Development of multi-objective reservoir operation rules for integrated water resources management
Cheong, T. S.,Ko, I.,Labadie, J. W. IWA Publishing 2010 Journal of hydroinformatics Vol.12 No.2
<P>Real-time monitoring, databases, optimization models and visualization tools have been integrated into a Decision Support System (DSS) for optimal water resources management of two water supply reservoirs, the Daechung Reservoir and the Yongdam Reservoir of the Geum River basin, Daejeon, Korea. The KModSim as a DSS has been designed to provide information on current reservoir conditions to operational staff and to help in making decisions for short- and long-term management. For the physical calibration, the network simulations in seasonal water allocation of both reservoirs are performed for 23 years from January 1 1983 to June 30 2006. Linear and nonlinear operating rules are developed by using the actual reservoir operation data obtained from both reservoirs which are then used in KModSim by the hydrologic state method to estimate optimized target storages of both reservoirs. For validation of hydrologic states in KModSim and scenario testing for the management simulations, the optimal network simulation for the seasonal water allocations from October 1 2002 to June 30 2006 were also performed. The results' simulation by new rules fit the measured actual reservoir storage and represent well the various outflow discharge curves measured at the gauging stations of Geum River. The developed operating rules are proven to be superior in explaining actual reservoir operation as compared to the simulated target storages by existing optimization models.</P>
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Integrated Flood Control System for HAN River Basin
Shim, Soon-Bo,Shim, Kyu-Cheoul,Fontane, Darrell G.,Labadie, John W. 충북대학교 건설기술연구소 2004 建設技術論文集 Vol.23 No.1
본 논문은 공간의사결정지원시스템의 시범시스템으로서, 다목적 저수지군의 실시간 통합유역홍수조절 시스템에 대한 연구이다. 본 공간의사결정지원시스템은 자료관리시스템, 실시간 기상 및 수문자료 모니터링시스템, 저수지군의 모의 및 최적화를 모형기반시스템, 시스템의 효율적인 사용을 위한 사용자편의시스템으로 구성되어 있다. 홍수사상 동안에 수시로 갱신되는 홍수 수문자료의 공간적인 분포를 예측하여 제공하기 위하며, 신경망 알고리즘이 사용되었다. 예측자료는 유역 저수지군의 게이트조절을 위한 최적화 전략수립을 위한 입력자료로서 활용된다. 본 시스템은 1995년 한강유역의 홍수사상에 적용되었으며, 하류의 홍수피해를 최소화하면서 홍수기 이후의 수자원확보를 최대화할 수 있는 운영률을 제시하였다. A prototype spatial decision support system (SDSS) is presented for integrated, real-time river basin food control in a multipurpose, multireservoir system. The SDSS integrates a GIS with a database management subsystem, a real-time meteorological and hydrological data monitoring system, a model-base subsystem for system simulation and optimization, and a graphical dialog interface allowing effective use by system operators. The model-base subsystem employs an artificial neural network in a real-time flood forecasting module providing spatially distributed forecasted flows that are updated as the flood event progresses. Forecasted, basin-wide discharges are input into a dynamic programming module providing optimal gate control strategies, which are also updated in real-time. The SDSS for flood control is applied to the Han River Basin in Korea and demonstrated through simulated application to a severe 1995 flood event. Results of the case study indicate that integrated operational strategies generated by the SDSS for flood control substantially reduce downstream flood impacts, while maintaining sufficient conservation storage for water use subsequent to the flood seas.
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Kullback–Leibler divergence for Bayesian nonparametric model checking
Al-Labadi Luai,Patel Vishakh,Vakiloroayaei Kasra,Wan Clement 한국통계학회 2021 Journal of the Korean Statistical Society Vol.50 No.1
Bayesian nonparametric statistics is an area of considerable research interest. While recently there has been an extensive concentration in developing Bayesian nonparametric procedures for model checking, the use of the Dirichlet process, in its simplest form, along with the Kullback–Leibler divergence is still an open problem. This is mainly attributed to the discreteness property of the Dirichlet process and that the Kullback–Leibler divergence between any discrete distribution and any continuous distribution is infnity. The approach proposed in this paper, which is based on incorporating the Dirichlet process, the Kullback–Leibler divergence and the relative belief ratio, is considered the frst concrete solution to this issue. Applying the approach is simple and does not require obtaining a closed form of the relative belief ratio. A Monte Carlo study and real data examples show that the developed approach exhibits excellent performance.
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Al-Labadi Luai,Asl Forough Fazeli 한국통계학회 2022 Journal of the Korean Statistical Society Vol.51 No.3
The relative belief ratio becomes a widespread tool in many hypothesis testing problems. It measures the statistical evidence that a given statement is true based on a combination of data, model and prior. Additionally, a measure of the strength is used to calibrate its value. In this paper, robustness of the relative belief ratio and its strength to the choice of the prior is studied. Specifically, the Gâteaux derivative is used to measure their sensitivity when the geometric contaminated prior is used. Examples are presented to illustrate the results.
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