Research on Combination Forecast Mode of Conceptual Hydrological Model
The calibration and selection of conceptual hydrological model parameters is an important but complex task in runoff forecasting. In order to solve the calibration of conceptual hydrological model parameters, a multi-objective cultural self-adaptive electromagnetism-like mechanism algorithm (MOCSEM)...
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Veröffentlicht in: | Water resources management 2016-10, Vol.30 (13), p.4483-4499 |
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description | The calibration and selection of conceptual hydrological model parameters is an important but complex task in runoff forecasting. In order to solve the calibration of conceptual hydrological model parameters, a multi-objective cultural self-adaptive electromagnetism-like mechanism algorithm (MOCSEM) is proposed in this paper. The multi-objective parameter calibration method of runoff forecasting avoids the “averaging effect” and considers both large and small runoffs hydrological features. In this paper, the self-identifying parameter combination forecasting method (SPCFM), a universality combination forecast model, is developed innovatively to improve forecasting precision by using the extreme parameters of Pareto optimal solutions. Finally, MOCSEM is combined with SPCFM to calibrate the parameters of forecasting model and forecast runoff of Leaf River. The results indicate that the proposed methods improve forecast accuracy and provide an effective approach to runoff forecast. |
doi_str_mv | 10.1007/s11269-016-1401-z |
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In order to solve the calibration of conceptual hydrological model parameters, a multi-objective cultural self-adaptive electromagnetism-like mechanism algorithm (MOCSEM) is proposed in this paper. The multi-objective parameter calibration method of runoff forecasting avoids the “averaging effect” and considers both large and small runoffs hydrological features. In this paper, the self-identifying parameter combination forecasting method (SPCFM), a universality combination forecast model, is developed innovatively to improve forecasting precision by using the extreme parameters of Pareto optimal solutions. Finally, MOCSEM is combined with SPCFM to calibrate the parameters of forecasting model and forecast runoff of Leaf River. The results indicate that the proposed methods improve forecast accuracy and provide an effective approach to runoff forecast.</description><identifier>ISSN: 0920-4741</identifier><identifier>EISSN: 1573-1650</identifier><identifier>DOI: 10.1007/s11269-016-1401-z</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Algorithms ; Atmospheric Sciences ; Calibration ; Civil Engineering ; Earth and Environmental Science ; Earth Sciences ; Electromagnetism ; Environment ; Forecasting ; Freshwater ; Geotechnical Engineering & Applied Earth Sciences ; Hydrogeology ; Hydrologic cycle ; Hydrologic models ; Hydrologic sciences ; Hydrology ; Hydrology models ; Hydrology/Water Resources ; Mathematical models ; Methods ; Multiple objective ; Optimization ; Parameters ; Pareto optimum ; Precipitation ; Rain ; Runoff ; Runoff forecasting ; Studies ; Tasks ; Water resources ; Water resources management ; Watersheds</subject><ispartof>Water resources management, 2016-10, Vol.30 (13), p.4483-4499</ispartof><rights>Springer Science+Business Media Dordrecht 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c382t-915966ab0533cf881c385088ace5f8ab1c3c4fbf5db2c503ed0cac02381a57a33</citedby><cites>FETCH-LOGICAL-c382t-915966ab0533cf881c385088ace5f8ab1c3c4fbf5db2c503ed0cac02381a57a33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11269-016-1401-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11269-016-1401-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids></links><search><creatorcontrib>Dai, Minglong</creatorcontrib><creatorcontrib>Zhou, Jianzhong</creatorcontrib><creatorcontrib>Liao, Xiang</creatorcontrib><title>Research on Combination Forecast Mode of Conceptual Hydrological Model</title><title>Water resources management</title><addtitle>Water Resour Manage</addtitle><description>The calibration and selection of conceptual hydrological model parameters is an important but complex task in runoff forecasting. In order to solve the calibration of conceptual hydrological model parameters, a multi-objective cultural self-adaptive electromagnetism-like mechanism algorithm (MOCSEM) is proposed in this paper. The multi-objective parameter calibration method of runoff forecasting avoids the “averaging effect” and considers both large and small runoffs hydrological features. In this paper, the self-identifying parameter combination forecasting method (SPCFM), a universality combination forecast model, is developed innovatively to improve forecasting precision by using the extreme parameters of Pareto optimal solutions. Finally, MOCSEM is combined with SPCFM to calibrate the parameters of forecasting model and forecast runoff of Leaf River. The results indicate that the proposed methods improve forecast accuracy and provide an effective approach to runoff forecast.</description><subject>Algorithms</subject><subject>Atmospheric Sciences</subject><subject>Calibration</subject><subject>Civil Engineering</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Electromagnetism</subject><subject>Environment</subject><subject>Forecasting</subject><subject>Freshwater</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Hydrogeology</subject><subject>Hydrologic cycle</subject><subject>Hydrologic models</subject><subject>Hydrologic sciences</subject><subject>Hydrology</subject><subject>Hydrology models</subject><subject>Hydrology/Water Resources</subject><subject>Mathematical models</subject><subject>Methods</subject><subject>Multiple objective</subject><subject>Optimization</subject><subject>Parameters</subject><subject>Pareto optimum</subject><subject>Precipitation</subject><subject>Rain</subject><subject>Runoff</subject><subject>Runoff forecasting</subject><subject>Studies</subject><subject>Tasks</subject><subject>Water resources</subject><subject>Water resources 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runoff forecasting. In order to solve the calibration of conceptual hydrological model parameters, a multi-objective cultural self-adaptive electromagnetism-like mechanism algorithm (MOCSEM) is proposed in this paper. The multi-objective parameter calibration method of runoff forecasting avoids the “averaging effect” and considers both large and small runoffs hydrological features. In this paper, the self-identifying parameter combination forecasting method (SPCFM), a universality combination forecast model, is developed innovatively to improve forecasting precision by using the extreme parameters of Pareto optimal solutions. Finally, MOCSEM is combined with SPCFM to calibrate the parameters of forecasting model and forecast runoff of Leaf River. The results indicate that the proposed methods improve forecast accuracy and provide an effective approach to runoff forecast.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11269-016-1401-z</doi><tpages>17</tpages></addata></record> |
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subjects | Algorithms Atmospheric Sciences Calibration Civil Engineering Earth and Environmental Science Earth Sciences Electromagnetism Environment Forecasting Freshwater Geotechnical Engineering & Applied Earth Sciences Hydrogeology Hydrologic cycle Hydrologic models Hydrologic sciences Hydrology Hydrology models Hydrology/Water Resources Mathematical models Methods Multiple objective Optimization Parameters Pareto optimum Precipitation Rain Runoff Runoff forecasting Studies Tasks Water resources Water resources management Watersheds |
title | Research on Combination Forecast Mode of Conceptual Hydrological Model |
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