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Optimizing Operational Policies of a Korean Multireservoir System Using Sampling Stochastic Dynamic Programming with Ensemble Streamflow Prediction

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dc.contributor.authorKim, Young-Oh-
dc.contributor.authorEum, Hyung-Il-
dc.contributor.authorLee, Eun-Goo-
dc.contributor.authorKo, Ick Hwan-
dc.date.accessioned2010-06-21T23:20:47Z-
dc.date.available2010-06-21T23:20:47Z-
dc.date.issued2007-
dc.identifier.citationJournal of Water Resources Planning and Management, 133(1), 4-14en
dc.identifier.issn0733-9496-
dc.identifier.urihttp://hdl.handle.net/10371/67725-
dc.description.abstractThis study presents state-of-the-art optimization techniques for enhancing reservoir operations which use sampling stochastic
dynamic programming SSDP with ensemble streamflow prediction ESP . SSDP used with historical inflow scenarios SSDP/Hist
derives an off-line optimal operating policy through a backward-moving solution procedure. In contrast, SSDP used with monthly
forecasts of ESP SSSDP/ESP reoptimizes the off-line policy. These stochastic models are used to derive a monthly joint operating policy
during the drawdown period of the Geum River multireservoir system in Korea. A cross-validation test of 1,900 simulation runs
demonstrates that: 1 proposed stochastic models that explicitly include inflow uncertainty are superior to those that do not; 2 updating
policy with ESP forecasts is appropriate in this reservoir system; 3 the lower dam of the Geum River multireservoir system should
maintain elevation of 66.5 m during the beginning of the drawdown period to avoid significant increase in the downstream water
shortages; and 4 forecasting accuracy may result in considerable effects on joint reservoir operations.
en
dc.language.isoenen
dc.publisherAmerican Society of Civil Engineersen
dc.subjectOptimizationen
dc.subjectForecastingen
dc.subjectReservoir operationen
dc.subjectKoreaen
dc.subjectStreamflowen
dc.subjectPredictionsen
dc.titleOptimizing Operational Policies of a Korean Multireservoir System Using Sampling Stochastic Dynamic Programming with Ensemble Streamflow Predictionen
dc.typeArticleen
dc.contributor.AlternativeAuthor김영오-
dc.contributor.AlternativeAuthor엄형일-
dc.contributor.AlternativeAuthor이은구-
dc.contributor.AlternativeAuthor고익환-
dc.identifier.doi10.1061/(ASCE)0733-9496(2007)133:1(4)-
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Civil & Environmental Engineering (건설환경공학부)Journal Papers (저널논문_건설환경공학부)
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