Mostrar el registro sencillo del ítem
dc.contributor.author | Macian-Sorribes, Hector![]() |
es_ES |
dc.contributor.author | Pulido-Velazquez, M.![]() |
es_ES |
dc.date.accessioned | 2020-04-08T05:58:43Z | |
dc.date.available | 2020-04-08T05:58:43Z | |
dc.date.issued | 2019-10-20 | es_ES |
dc.identifier.issn | 2049-1948 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/140500 | |
dc.description.abstract | [EN] Coordinated and efficient operation of water resource systems becomes essential to deal with growing demands and uncertain resources in water-stressed regions. System analysis models and tools help address the complexities of multireservoir systems when defining operating rules. This paper reviews the state of the art in developing operating rules for multireservoir water resource systems, focusing on efficient system operation. This review focuses on how optimal operating rules can be derived and represented. Advantages and drawbacks of each approach are discussed. Major approaches to derive optimal operating rules include direct optimization of reservoir operation, embedding conditional operating rules in simulation-optimization frameworks, and inferring rules from optimization results. Suggestions on which approach to use depend on context. Parametrization-simulation-optimization or rule inference using heuristics are promising approaches. Increased forecasting capabilities will further benefit the use of model predictive control algorithms to improve system operation. This article is categorized under: Engineering Water > Water, Health, and Sanitation Engineering Water > Methods | es_ES |
dc.description.sponsorship | The study has been partially funded by the ADAPTAMED project (RTI2018-101483-B-I00) from the Ministerio de Ciencia, Innovacion Universidades (MICINN) of Spain, and by the postdoctoral program (PAID-10-18) of the Universitat Politecnica de Valencia (UPV). | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | John Wiley & Sons | es_ES |
dc.relation.ispartof | Wiley Interdisciplinary Reviews Water | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Optimization | es_ES |
dc.subject | Reservoir operation | es_ES |
dc.subject | Stochastic programming | es_ES |
dc.subject | Water resources management | es_ES |
dc.subject.classification | INGENIERIA HIDRAULICA | es_ES |
dc.title | Inferring efficient operating rules in multireservoir water resource systems: A review | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1002/wat2.1400 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/UPV//PAID-10-18/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-101483-B-I00/ES/PLANIFICACION, DISEÑO Y EVALUACION DE LA ADAPTACION DE CUENCAS MEDITERRANEAS A ESCENARIOS SOCIOECONOMICOS Y DE CAMBIO CLIMATICO/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient | es_ES |
dc.description.bibliographicCitation | Macian-Sorribes, H.; Pulido-Velazquez, M. (2019). Inferring efficient operating rules in multireservoir water resource systems: A review. Wiley Interdisciplinary Reviews Water. 7(1):1-24. https://doi.org/10.1002/wat2.1400 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1002/wat2.1400 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 24 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 7 | es_ES |
dc.description.issue | 1 | es_ES |
dc.relation.pasarela | S\400735 | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |
dc.description.references | Aboutalebi, M., Bozorg Haddad, O., & Loáiciga, H. A. (2015). Optimal Monthly Reservoir Operation Rules for Hydropower Generation Derived with SVR-NSGAII. Journal of Water Resources Planning and Management, 141(11), 04015029. doi:10.1061/(asce)wr.1943-5452.0000553 | es_ES |
dc.description.references | Ahmad, A., El-Shafie, A., Razali, S. F. M., & Mohamad, Z. S. (2014). Reservoir Optimization in Water Resources: a Review. Water Resources Management, 28(11), 3391-3405. doi:10.1007/s11269-014-0700-5 | es_ES |
dc.description.references | Ahmadi, M., Bozorg Haddad, O., & Mariño, M. A. (2013). Extraction of Flexible Multi-Objective Real-Time Reservoir Operation Rules. Water Resources Management, 28(1), 131-147. doi:10.1007/s11269-013-0476-z | es_ES |
dc.description.references | Andreu, J., Capilla, J., & Sanchís, E. (1996). AQUATOOL, a generalized decision-support system for water-resources planning and operational management. Journal of Hydrology, 177(3-4), 269-291. doi:10.1016/0022-1694(95)02963-x | es_ES |
dc.description.references | Andreu, J., & Sahuquillo, A. (1987). Efficient Aquifer Simulation in Complex Systems. Journal of Water Resources Planning and Management, 113(1), 110-129. doi:10.1061/(asce)0733-9496(1987)113:1(110) | es_ES |
dc.description.references | Ashbolt, S. C., Maheepala, S., & Perera, B. J. C. (2016). Using Multiobjective Optimization to Find Optimal Operating Rules for Short-Term Planning of Water Grids. Journal of Water Resources Planning and Management, 142(10), 04016033. doi:10.1061/(asce)wr.1943-5452.0000675 | es_ES |
dc.description.references | Ashbolt, S. C., & Perera, B. J. C. (2018). Multiobjective Optimization of Seasonal Operating Rules for Water Grids Using Streamflow Forecast Information. Journal of Water Resources Planning and Management, 144(4), 05018003. doi:10.1061/(asce)wr.1943-5452.0000902 | es_ES |
dc.description.references | Azari, A., Hamzeh, S., & Naderi, S. (2018). Multi-Objective Optimization of the Reservoir System Operation by Using the Hedging Policy. Water Resources Management, 32(6), 2061-2078. doi:10.1007/s11269-018-1917-5 | es_ES |
dc.description.references | Becker, L., & Yeh, W. W.-G. (1974). Optimization of real time operation of a multiple-reservoir system. Water Resources Research, 10(6), 1107-1112. doi:10.1029/wr010i006p01107 | es_ES |
dc.description.references | Bellman, R. E., & Dreyfus, S. E. (1962). Applied Dynamic Programming. doi:10.1515/9781400874651 | es_ES |
dc.description.references | Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust Optimization. doi:10.1515/9781400831050 | es_ES |
dc.description.references | Bessler, F. T., Savic, D. A., & Walters, G. A. (2003). Water Reservoir Control with Data Mining. Journal of Water Resources Planning and Management, 129(1), 26-34. doi:10.1061/(asce)0733-9496(2003)129:1(26) | es_ES |
dc.description.references | Bhaskar, N. R., & Whitlatch, E. E. (1980). Derivation of monthly reservoir release policies. Water Resources Research, 16(6), 987-993. doi:10.1029/wr016i006p00987 | es_ES |
dc.description.references | Bianucci, P., Sordo-Ward, Á., Moralo, J., & Garrote, L. (2015). Probabilistic-Multiobjective Comparison of User-Defined Operating Rules. Case Study: Hydropower Dam in Spain. Water, 7(12), 956-974. doi:10.3390/w7030956 | es_ES |
dc.description.references | Biglarbeigi, P., Giuliani, M., & Castelletti, A. (2018). Partitioning the Impacts of Streamflow and Evaporation Uncertainty on the Operations of Multipurpose Reservoirs in Arid Regions. Journal of Water Resources Planning and Management, 144(7), 05018008. doi:10.1061/(asce)wr.1943-5452.0000945 | es_ES |
dc.description.references | Bolouri-Yazdeli, Y., Bozorg Haddad, O., Fallah-Mehdipour, E., & Mariño, M. A. (2014). Evaluation of Real-Time Operation Rules in Reservoir Systems Operation. Water Resources Management, 28(3), 715-729. doi:10.1007/s11269-013-0510-1 | es_ES |
dc.description.references | Borgomeo, E., Mortazavi-Naeini, M., Hall, J. W., O’Sullivan, M. J., & Watson, T. (2016). Trading-off tolerable risk with climate change adaptation costs in water supply systems. Water Resources Research, 52(2), 622-643. doi:10.1002/2015wr018164 | es_ES |
dc.description.references | Bozorg-Haddad, O., Azarnivand, A., Hosseini-Moghari, S.-M., & Loáiciga, H. A. (2017). WASPAS Application and Evolutionary Algorithm Benchmarking in Optimal Reservoir Optimization Problems. Journal of Water Resources Planning and Management, 143(1), 04016070. doi:10.1061/(asce)wr.1943-5452.0000716 | es_ES |
dc.description.references | Bozorg-Haddad, O., Karimirad, I., Seifollahi-Aghmiuni, S., & Loáiciga, H. A. (2015). Development and Application of the Bat Algorithm for Optimizing the Operation of Reservoir Systems. Journal of Water Resources Planning and Management, 141(8), 04014097. doi:10.1061/(asce)wr.1943-5452.0000498 | es_ES |
dc.description.references | Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324 | es_ES |
dc.description.references | Brown, C., Ghile, Y., Laverty, M., & Li, K. (2012). Decision scaling: Linking bottom-up vulnerability analysis with climate projections in the water sector. Water Resources Research, 48(9). doi:10.1029/2011wr011212 | es_ES |
dc.description.references | Brown, C. M., Lund, J. R., Cai, X., Reed, P. M., Zagona, E. A., Ostfeld, A., … Brekke, L. (2015). The future of water resources systems analysis: Toward a scientific framework for sustainable water management. Water Resources Research, 51(8), 6110-6124. doi:10.1002/2015wr017114 | es_ES |
dc.description.references | Cai, X., McKinney, D. C., & Lasdon, L. S. (2001). Piece-by-Piece Approach to Solving Large Nonlinear Water Resources Management Models. Journal of Water Resources Planning and Management, 127(6), 363-368. doi:10.1061/(asce)0733-9496(2001)127:6(363) | es_ES |
dc.description.references | Cai, X., Vogel, R., & Ranjithan, R. (2013). Special Issue on the Role of Systems Analysis in Watershed Management. Journal of Water Resources Planning and Management, 139(5), 461-463. doi:10.1061/(asce)wr.1943-5452.0000341 | es_ES |
dc.description.references | Cancelliere, A., Giuliano, G., Ancarani, A., & Rossi, G. (2002). Water Resources Management, 16(1), 71-88. doi:10.1023/a:1015563820136 | es_ES |
dc.description.references | Caseri, A., Javelle, P., Ramos, M. H., & Leblois, E. (2015). Generating precipitation ensembles for flood alert and risk management. Journal of Flood Risk Management, 9(4), 402-415. doi:10.1111/jfr3.12203 | es_ES |
dc.description.references | Castelletti, A., Galelli, S., Restelli, M., & Soncini-Sessa, R. (2010). Tree-based reinforcement learning for optimal water reservoir operation. Water Resources Research, 46(9). doi:10.1029/2009wr008898 | es_ES |
dc.description.references | Castelletti, A., Pianosi, F., & Restelli, M. (2013). A multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run. Water Resources Research, 49(6), 3476-3486. doi:10.1002/wrcr.20295 | es_ES |
dc.description.references | Castelletti, A., Pianosi, F., & Soncini-Sessa, R. (2008). Water reservoir control under economic, social and environmental constraints. Automatica, 44(6), 1595-1607. doi:10.1016/j.automatica.2008.03.003 | es_ES |
dc.description.references | Castelletti, A., & Soncini-Sessa, R. (2007). Bayesian networks in water resource modelling and management. Environmental Modelling & Software, 22(8), 1073-1074. doi:10.1016/j.envsoft.2006.06.001 | es_ES |
dc.description.references | Castelletti, A., & Soncini-Sessa, R. (2007). Bayesian Networks and participatory modelling in water resource management. Environmental Modelling & Software, 22(8), 1075-1088. doi:10.1016/j.envsoft.2006.06.003 | es_ES |
dc.description.references | Celeste, A. B., & Billib, M. (2009). Evaluation of stochastic reservoir operation optimization models. Advances in Water Resources, 32(9), 1429-1443. doi:10.1016/j.advwatres.2009.06.008 | es_ES |
dc.description.references | Celeste, A. B., Curi, W. F., & Curi, R. C. (2009). Implicit Stochastic Optimization for deriving reservoir operating rules in semiarid Brazil. Pesquisa Operacional, 29(1), 223-234. doi:10.1590/s0101-74382009000100011 | es_ES |
dc.description.references | Chandramouli, V., & Raman, H. (2001). Multireservoir Modeling with Dynamic Programming and Neural Networks. Journal of Water Resources Planning and Management, 127(2), 89-98. doi:10.1061/(asce)0733-9496(2001)127:2(89) | es_ES |
dc.description.references | Chang, L.-C., & Chang, F.-J. (2001). Intelligent control for modelling of real-time reservoir operation. Hydrological Processes, 15(9), 1621-1634. doi:10.1002/hyp.226 | es_ES |
dc.description.references | Chazarra, M., García-González, J., Pérez-Díaz, J. I., & Arteseros, M. (2016). Stochastic optimization model for the weekly scheduling of a hydropower system in day-ahead and secondary regulation reserve markets. Electric Power Systems Research, 130, 67-77. doi:10.1016/j.epsr.2015.08.014 | es_ES |
dc.description.references | Chen, D., Leon, A. S., Fuentes, C., Gibson, N. L., & Qin, H. (2018). Incorporating Filters in Random Search Algorithms for the Hourly Operation of a Multireservoir System. Journal of Water Resources Planning and Management, 144(2), 04017088. doi:10.1061/(asce)wr.1943-5452.0000876 | es_ES |
dc.description.references | Coerver, H. M., Rutten, M. M., & van de Giesen, N. C. (2018). Deduction of reservoir operating rules for application in global hydrological models. Hydrology and Earth System Sciences, 22(1), 831-851. doi:10.5194/hess-22-831-2018 | es_ES |
dc.description.references | Côté, P., & Leconte, R. (2016). Comparison of Stochastic Optimization Algorithms for Hydropower Reservoir Operation with Ensemble Streamflow Prediction. Journal of Water Resources Planning and Management, 142(2), 04015046. doi:10.1061/(asce)wr.1943-5452.0000575 | es_ES |
dc.description.references | Cui, L., & Kuczera, G. (2005). Optimizing water supply headworks operating rules under stochastic inputs: Assessment of genetic algorithm performance. Water Resources Research, 41(5). doi:10.1029/2004wr003517 | es_ES |
dc.description.references | Culley, S., Noble, S., Yates, A., Timbs, M., Westra, S., Maier, H. R., … Castelletti, A. (2016). A bottom-up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate. Water Resources Research, 52(9), 6751-6768. doi:10.1002/2015wr018253 | es_ES |
dc.description.references | Cunha, M. C., & Antunes, A. (2012). Simulated annealing algorithms for water systems optimization. WIT Transactions on State of the Art in Science and Engineering, 57-73. doi:10.2495/978-1-84564-664-6/04 | es_ES |
dc.description.references | Dariane, A. B., & Momtahen, S. (2009). Optimization of Multireservoir Systems Operation Using Modified Direct Search Genetic Algorithm. Journal of Water Resources Planning and Management, 135(3), 141-148. doi:10.1061/(asce)0733-9496(2009)135:3(141) | es_ES |
dc.description.references | Das, B., Singh, A., Panda, S. N., & Yasuda, H. (2015). Optimal land and water resources allocation policies for sustainable irrigated agriculture. Land Use Policy, 42, 527-537. doi:10.1016/j.landusepol.2014.09.012 | es_ES |
dc.description.references | Davidsen, C., Liu, S., Mo, X., Rosbjerg, D., & Bauer-Gottwein, P. (2016). The cost of ending groundwater overdraft on the North China Plain. Hydrology and Earth System Sciences, 20(2), 771-785. doi:10.5194/hess-20-771-2016 | es_ES |
dc.description.references | Ehteram, M., Karami, H., & Farzin, S. (2018). Reservoir Optimization for Energy Production Using a New Evolutionary Algorithm Based on Multi-Criteria Decision-Making Models. Water Resources Management, 32(7), 2539-2560. doi:10.1007/s11269-018-1945-1 | es_ES |
dc.description.references | Eisel, L. M. (1972). Chance constrained reservoir model. Water Resources Research, 8(2), 339-347. doi:10.1029/wr008i002p00339 | es_ES |
dc.description.references | European Commission(2007). Communication from the Commission to the European Parliament and the Council: Addressing the challenge of water scarcity and droughts in the European Union COM(2007) 414 final. Brussels Belgium. | es_ES |
dc.description.references | European Commission. (2012a). Communication from the Commission to the European Parliament the Council the European Economic and Social Committee and the Committee of the Regions: A Blueprint to Safeguard Europe's Water Resources COM(2012) 673 final. Brussels Belgium. | es_ES |
dc.description.references | European Commission. (2012b). Communication from the Commission to the European Parliament the Council the European Economic and Social Committee and the Committee of the Regions: Report on the Review of the European Water Scarcity and Droughts Policy COM(2012) 672 final. Brussels Belgium. | es_ES |
dc.description.references | Fallah-Mehdipour, E., Bozorg Haddad, O., & Mariño, M. A. (2012). Real-Time Operation of Reservoir System by Genetic Programming. Water Resources Management, 26(14), 4091-4103. doi:10.1007/s11269-012-0132-z | es_ES |
dc.description.references | Fazlali, A., & Shourian, M. (2017). A Demand Management Based Crop and Irrigation Planning Using the Simulation-Optimization Approach. Water Resources Management, 32(1), 67-81. doi:10.1007/s11269-017-1791-6 | es_ES |
dc.description.references | Ficchì, A., Raso, L., Dorchies, D., Pianosi, F., Malaterre, P.-O., Van Overloop, P.-J., & Jay-Allemand, M. (2016). Optimal Operation of the Multireservoir System in the Seine River Basin Using Deterministic and Ensemble Forecasts. Journal of Water Resources Planning and Management, 142(1), 05015005. doi:10.1061/(asce)wr.1943-5452.0000571 | es_ES |
dc.description.references | Fu, Q., Li, T., Cui, S., Liu, D., & Lu, X. (2017). Agricultural Multi-Water Source Allocation Model Based on Interval Two-Stage Stochastic Robust Programming under Uncertainty. Water Resources Management, 32(4), 1261-1274. doi:10.1007/s11269-017-1868-2 | es_ES |
dc.description.references | Galelli, S., Goedbloed, A., Schwanenberg, D., & van Overloop, P.-J. (2014). Optimal Real-Time Operation of Multipurpose Urban Reservoirs: Case Study in Singapore. Journal of Water Resources Planning and Management, 140(4), 511-523. doi:10.1061/(asce)wr.1943-5452.0000342 | es_ES |
dc.description.references | Giuliani, M., Castelletti, A., Pianosi, F., Mason, E., & Reed, P. M. (2016). Curses, Tradeoffs, and Scalable Management: Advancing Evolutionary Multiobjective Direct Policy Search to Improve Water Reservoir Operations. Journal of Water Resources Planning and Management, 142(2), 04015050. doi:10.1061/(asce)wr.1943-5452.0000570 | es_ES |
dc.description.references | Giuliani, M., Herman, J. D., Castelletti, A., & Reed, P. (2014). Many-objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management. Water Resources Research, 50(4), 3355-3377. doi:10.1002/2013wr014700 | es_ES |
dc.description.references | Giuliani, M., Li, Y., Castelletti, A., & Gandolfi, C. (2016). A coupled human-natural systems analysis of irrigated agriculture under changing climate. Water Resources Research, 52(9), 6928-6947. doi:10.1002/2016wr019363 | es_ES |
dc.description.references | Giuliani, M., Quinn, J. D., Herman, J. D., Castelletti, A., & Reed, P. M. (2018). Scalable Multiobjective Control for Large-Scale Water Resources Systems Under Uncertainty. IEEE Transactions on Control Systems Technology, 26(4), 1492-1499. doi:10.1109/tcst.2017.2705162 | es_ES |
dc.description.references | Grüne, L., & Semmler, W. (2004). Using dynamic programming with adaptive grid scheme for optimal control problems in economics. Journal of Economic Dynamics and Control, 28(12), 2427-2456. doi:10.1016/j.jedc.2003.11.002 | es_ES |
dc.description.references | Guariso, G., Rinaldi, S., & Soncini-Sessa, R. (1986). The Management of Lake Como: A Multiobjective Analysis. Water Resources Research, 22(2), 109-120. doi:10.1029/wr022i002p00109 | es_ES |
dc.description.references | Gundelach, J., & ReVelle, C. (1975). Linear decision rule in reservoir management and design: 5. A general algorithm. Water Resources Research, 11(2), 204-207. doi:10.1029/wr011i002p00204 | es_ES |
dc.description.references | Guo, X., Hu, T., Zeng, X., & Li, X. (2013). Extension of Parametric Rule with the Hedging Rule for Managing Multireservoir System during Droughts. Journal of Water Resources Planning and Management, 139(2), 139-148. doi:10.1061/(asce)wr.1943-5452.0000241 | es_ES |
dc.description.references | Haddad, O. B., Afshar, A., & Mariño, M. A. (2006). Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization. Water Resources Management, 20(5), 661-680. doi:10.1007/s11269-005-9001-3 | es_ES |
dc.description.references | Hadka, D., Herman, J., Reed, P., & Keller, K. (2015). An open source framework for many-objective robust decision making. Environmental Modelling & Software, 74, 114-129. doi:10.1016/j.envsoft.2015.07.014 | es_ES |
dc.description.references | Haguma, D., & Leconte, R. (2018). Long-Term Planning of Water Systems in the Context of Climate Non-Stationarity with Deterministic and Stochastic Optimization. Water Resources Management, 32(5), 1725-1739. doi:10.1007/s11269-017-1900-6 | es_ES |
dc.description.references | Haguma, D., Leconte, R., & Côté, P. (2018). Evaluating Transition Probabilities for a Stochastic Dynamic Programming Model Used in Water System Optimization. Journal of Water Resources Planning and Management, 144(2), 04017090. doi:10.1061/(asce)wr.1943-5452.0000883 | es_ES |
dc.description.references | Houck, M. H. (1979). A Chance Constrained Optimization Model for reservoir design and operation. Water Resources Research, 15(5), 1011-1016. doi:10.1029/wr015i005p01011 | es_ES |
dc.description.references | Ji, C., Zhou, T., & Huang, H. (2014). Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression. Water Resources Management, 28(9), 2435-2451. doi:10.1007/s11269-014-0610-6 | es_ES |
dc.description.references | Karamouz, M., & Houck, M. H. (1982). Annual and monthly reservoir operating rules generated by deterministic optimization. Water Resources Research, 18(5), 1337-1344. doi:10.1029/wr018i005p01337 | es_ES |
dc.description.references | Karamouz, M., & Houck, M. H. (1987). COMPARISON OF STOCHASTIC AND DETERMINISTIC DYNAMIC PROGRAMMING FOR RESERVOIR OPERATING RULE GENERATION. Journal of the American Water Resources Association, 23(1), 1-9. doi:10.1111/j.1752-1688.1987.tb00778.x | es_ES |
dc.description.references | Karamouz, M., & Vasiliadis, H. V. (1992). Bayesian stochastic optimization of reservoir operation using uncertain forecasts. Water Resources Research, 28(5), 1221-1232. doi:10.1029/92wr00103 | es_ES |
dc.description.references | Kasprzyk, J. R., Nataraj, S., Reed, P. M., & Lempert, R. J. (2013). Many objective robust decision making for complex environmental systems undergoing change. Environmental Modelling & Software, 42, 55-71. doi:10.1016/j.envsoft.2012.12.007 | es_ES |
dc.description.references | Kelman, J., Stedinger, J. R., Cooper, L. A., Hsu, E., & Yuan, S.-Q. (1990). Sampling stochastic dynamic programming applied to reservoir operation. Water Resources Research, 26(3), 447-454. doi:10.1029/wr026i003p00447 | es_ES |
dc.description.references | Keshtkar, A. R., Salajegheh, A., Sadoddin, A., & Allan, M. G. (2013). Application of Bayesian networks for sustainability assessment in catchment modeling and management (Case study: The Hablehrood river catchment). Ecological Modelling, 268, 48-54. doi:10.1016/j.ecolmodel.2013.08.003 | es_ES |
dc.description.references | Kim, T., Heo, J.-H., Bae, D.-H., & Kim, J.-H. (2008). Single-reservoir operating rules for a year using multiobjective genetic algorithm. Journal of Hydroinformatics, 10(2), 163-179. doi:10.2166/hydro.2008.019 | es_ES |
dc.description.references | Koutsoyiannis, D., & Economou, A. (2003). Evaluation of the parameterization-simulation-optimization approach for the control of reservoir systems. Water Resources Research, 39(6). doi:10.1029/2003wr002148 | es_ES |
dc.description.references | Kumar, D. N., & Reddy, M. J. (2006). Ant Colony Optimization for Multi-Purpose Reservoir Operation. Water Resources Management, 20(6), 879-898. doi:10.1007/s11269-005-9012-0 | es_ES |
dc.description.references | Nagesh Kumar, D., & Janga Reddy, M. (2007). Multipurpose Reservoir Operation Using Particle Swarm Optimization. Journal of Water Resources Planning and Management, 133(3), 192-201. doi:10.1061/(asce)0733-9496(2007)133:3(192) | es_ES |
dc.description.references | Kumar, K., & Kasthurirengan, S. (2018). Generalized Linear Two-Point Hedging Rule for Water Supply Reservoir Operation. Journal of Water Resources Planning and Management, 144(9), 04018051. doi:10.1061/(asce)wr.1943-5452.0000964 | es_ES |
dc.description.references | Kwakkel, J. H., Haasnoot, M., & Walker, W. E. (2016). Comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty. Environmental Modelling & Software, 86, 168-183. doi:10.1016/j.envsoft.2016.09.017 | es_ES |
dc.description.references | Labadie, J. W. (2004). Optimal Operation of Multireservoir Systems: State-of-the-Art Review. Journal of Water Resources Planning and Management, 130(2), 93-111. doi:10.1061/(asce)0733-9496(2004)130:2(93) | es_ES |
dc.description.references | Labadie J. W. Baldo M. &Larson R.(2000).MODSIM: Decision support system for river basin management. Documentation and user manual. | es_ES |
dc.description.references | Lee, J.-H., & Labadie, J. W. (2007). Stochastic optimization of multireservoir systems via reinforcement learning. Water Resources Research, 43(11). doi:10.1029/2006wr005627 | es_ES |
dc.description.references | Lei, X., Tan, Q., Wang, X., Wang, H., Wen, X., Wang, C., & Zhang, J. (2018). Stochastic optimal operation of reservoirs based on copula functions. Journal of Hydrology, 557, 265-275. doi:10.1016/j.jhydrol.2017.12.038 | es_ES |
dc.description.references | Lerma, N., Paredes-Arquiola, J., Andreu, J., & Solera, A. (2013). Development of operating rules for a complex multi-reservoir system by coupling genetic algorithms and network optimization. Hydrological Sciences Journal, 58(4), 797-812. doi:10.1080/02626667.2013.779777 | es_ES |
dc.description.references | Lerma, N., Paredes-Arquiola, J., Andreu, J., Solera, A., & Sechi, G. M. (2015). Assessment of evolutionary algorithms for optimal operating rules design in real Water Resource Systems. Environmental Modelling & Software, 69, 425-436. doi:10.1016/j.envsoft.2014.09.024 | es_ES |
dc.description.references | Li, Y., Giuliani, M., & Castelletti, A. (2017). A coupled human–natural system to assess the operational value of weather and climate services for agriculture. Hydrology and Earth System Sciences, 21(9), 4693-4709. doi:10.5194/hess-21-4693-2017 | es_ES |
dc.description.references | Lin, N. M., & Rutten, M. (2016). Optimal Operation of a Network of Multi-purpose Reservoir: A Review. Procedia Engineering, 154, 1376-1384. doi:10.1016/j.proeng.2016.07.504 | es_ES |
dc.description.references | Liu, P., Cai, X., & Guo, S. (2011). Deriving multiple near-optimal solutions to deterministic reservoir operation problems. Water Resources Research, 47(8). doi:10.1029/2011wr010998 | es_ES |
dc.description.references | Loucks, D. P. (1970). Some Comments on Linear Decision Rules and Chance Constraints. Water Resources Research, 6(2), 668-671. doi:10.1029/wr006i002p00668 | es_ES |
dc.description.references | Loucks, D. P. (2017). Managing Water as a Critical Component of a Changing World. Water Resources Management, 31(10), 2905-2916. doi:10.1007/s11269-017-1705-7 | es_ES |
dc.description.references | Lund J. R.(1996).Developing seasonal and long‐term reservoir system operation plans using HEC‐PRM Davis CA. | es_ES |
dc.description.references | Lund, J. R., & Ferreira, I. (1996). Operating Rule Optimization for Missouri River Reservoir System. Journal of Water Resources Planning and Management, 122(4), 287-295. doi:10.1061/(asce)0733-9496(1996)122:4(287) | es_ES |
dc.description.references | Lund, J. R., & Guzman, J. (1999). Derived Operating Rules for Reservoirs in Series or in Parallel. Journal of Water Resources Planning and Management, 125(3), 143-153. doi:10.1061/(asce)0733-9496(1999)125:3(143) | es_ES |
dc.description.references | Luo, B., Maqsood, I., & Huang, G. H. (2006). Planning water resources systems with interval stochastic dynamic programming. Water Resources Management, 21(6), 997-1014. doi:10.1007/s11269-006-9069-4 | es_ES |
dc.description.references | Macian‐Sorribes H.(2017).Design of optimal reservoir operating rules in large water resources systems combining stochastic programming fuzzy logic and expert criteria. Universitat Politècnica de València.https://doi.org/10.4995/THESIS/10251/82554. | es_ES |
dc.description.references | Macian-Sorribes, H., Tilmant, A., & Pulido-Velazquez, M. (2017). Improving operating policies of large-scale surface-groundwater systems through stochastic programming. Water Resources Research, 53(2), 1407-1423. doi:10.1002/2016wr019573 | es_ES |
dc.description.references | Maier, H. R., Guillaume, J. H. A., van Delden, H., Riddell, G. A., Haasnoot, M., & Kwakkel, J. H. (2016). An uncertain future, deep uncertainty, scenarios, robustness and adaptation: How do they fit together? Environmental Modelling & Software, 81, 154-164. doi:10.1016/j.envsoft.2016.03.014 | es_ES |
dc.description.references | Maier, H. R., Kapelan, Z., Kasprzyk, J., Kollat, J., Matott, L. S., Cunha, M. C., … Reed, P. M. (2014). Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions. Environmental Modelling & Software, 62, 271-299. doi:10.1016/j.envsoft.2014.09.013 | es_ES |
dc.description.references | Malekmohammadi, B., Kerachian, R., & Zahraie, B. (2009). Developing monthly operating rules for a cascade system of reservoirs: Application of Bayesian Networks. Environmental Modelling & Software, 24(12), 1420-1432. doi:10.1016/j.envsoft.2009.06.008 | es_ES |
dc.description.references | Mamdani, E. H. (1974). Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the Institution of Electrical Engineers, 121(12), 1585. doi:10.1049/piee.1974.0328 | es_ES |
dc.description.references | Mousavi, S. J., Ponnambalam, K., & Karray, F. (2007). Inferring operating rules for reservoir operations using fuzzy regression and ANFIS. Fuzzy Sets and Systems, 158(10), 1064-1082. doi:10.1016/j.fss.2006.10.024 | es_ES |
dc.description.references | Mujumdar, P. P., & Nirmala, B. (2006). A Bayesian Stochastic Optimization Model for a Multi-Reservoir Hydropower System. Water Resources Management, 21(9), 1465-1485. doi:10.1007/s11269-006-9094-3 | es_ES |
dc.description.references | Nalbantis, I., & Koutsoyiannis, D. (1997). A parametric rule for planning and management of multiple-reservoir systems. Water Resources Research, 33(9), 2165-2177. doi:10.1029/97wr01034 | es_ES |
dc.description.references | Nguyen, L., & Novák, V. (2019). Forecasting seasonal time series based on fuzzy techniques. Fuzzy Sets and Systems, 361, 114-129. doi:10.1016/j.fss.2018.09.010 | es_ES |
dc.description.references | Ostadrahimi, L., Mariño, M. A., & Afshar, A. (2011). Multi-reservoir Operation Rules: Multi-swarm PSO-based Optimization Approach. Water Resources Management, 26(2), 407-427. doi:10.1007/s11269-011-9924-9 | es_ES |
dc.description.references | Panigrahi, D. P., & Mujumdar, P. P. (2000). Water Resources Management, 14(2), 89-109. doi:10.1023/a:1008170632582 | es_ES |
dc.description.references | Pereira, M. V. F., & Pinto, L. M. V. G. (1985). Stochastic Optimization of a Multireservoir Hydroelectric System: A Decomposition Approach. Water Resources Research, 21(6), 779-792. doi:10.1029/wr021i006p00779 | es_ES |
dc.description.references | Pereira, M. V. F., & Pinto, L. M. V. G. (1991). Multi-stage stochastic optimization applied to energy planning. Mathematical Programming, 52(1-3), 359-375. doi:10.1007/bf01582895 | es_ES |
dc.description.references | Pereira-Cardenal, S. J., Mo, B., Riegels, N. D., Arnbjerg-Nielsen, K., & Bauer-Gottwein, P. (2015). Optimization of Multipurpose Reservoir Systems Using Power Market Models. Journal of Water Resources Planning and Management, 141(8), 04014100. doi:10.1061/(asce)wr.1943-5452.0000500 | es_ES |
dc.description.references | Philbrick, C. R., & Kitanidis, P. K. (1999). Limitations of Deterministic Optimization Applied to Reservoir Operations. Journal of Water Resources Planning and Management, 125(3), 135-142. doi:10.1061/(asce)0733-9496(1999)125:3(135) | es_ES |
dc.description.references | Pianosi, F., & Ravazzani, G. (2010). Assessing rainfall-runoff models for the management of Lake Verbano. Hydrological Processes, 24(22), 3195-3205. doi:10.1002/hyp.7745 | es_ES |
dc.description.references | Pianosi, F., & Soncini-Sessa, R. (2009). Real-time management of a multipurpose water reservoir with a heteroscedastic inflow model. Water Resources Research, 45(10). doi:10.1029/2008wr007335 | es_ES |
dc.description.references | Pulido-Velazquez, M., Andreu, J., Sahuquillo, A., & Pulido-Velazquez, D. (2008). Hydro-economic river basin modelling: The application of a holistic surface–groundwater model to assess opportunity costs of water use in Spain. Ecological Economics, 66(1), 51-65. doi:10.1016/j.ecolecon.2007.12.016 | es_ES |
dc.description.references | Pulido-Velazquez, M., Jenkins, M. W., & Lund, J. R. (2004). Economic values for conjunctive use and water banking in southern California. Water Resources Research, 40(3). doi:10.1029/2003wr002626 | es_ES |
dc.description.references | Qin, T., & Boccelli, D. L. (2019). Estimating Distribution System Water Demands Using Markov Chain Monte Carlo. Journal of Water Resources Planning and Management, 145(7), 04019023. doi:10.1061/(asce)wr.1943-5452.0001077 | es_ES |
dc.description.references | Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81-106. doi:10.1007/bf00116251 | es_ES |
dc.description.references | Reed, P. M., Hadka, D., Herman, J. D., Kasprzyk, J. R., & Kollat, J. B. (2013). Evolutionary multiobjective optimization in water resources: The past, present, and future. Advances in Water Resources, 51, 438-456. doi:10.1016/j.advwatres.2012.01.005 | es_ES |
dc.description.references | ReVelle, C., & Gundelach, J. (1975). Linear decision rule in reservoir management and design: 4. A rule that minimizes output variance. Water Resources Research, 11(2), 197-203. doi:10.1029/wr011i002p00197 | es_ES |
dc.description.references | Revelle, C., Joeres, E., & Kirby, W. (1969). The Linear Decision Rule in Reservoir Management and Design: 1, Development of the Stochastic Model. Water Resources Research, 5(4), 767-777. doi:10.1029/wr005i004p00767 | es_ES |
dc.description.references | Roach, T., Kapelan, Z., Ledbetter, R., & Ledbetter, M. (2016). Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty. Journal of Water Resources Planning and Management, 142(9), 04016028. doi:10.1061/(asce)wr.1943-5452.0000660 | es_ES |
dc.description.references | Rogers, P. P., & Fiering, M. B. (1986). Use of systems analysis in water management. Water Resources Research, 22(9S), 146S-158S. doi:10.1029/wr022i09sp0146s | es_ES |
dc.description.references | Ropero, R. F., Flores, M. J., Rumí, R., & Aguilera, P. A. (2016). Applications of hybrid dynamic Bayesian networks to water reservoir management. Environmetrics, 28(1), e2432. doi:10.1002/env.2432 | es_ES |
dc.description.references | Rougé, C., & Tilmant, A. (2016). Using stochastic dual dynamic programming in problems with multiple near-optimal solutions. Water Resources Research, 52(5), 4151-4163. doi:10.1002/2016wr018608 | es_ES |
dc.description.references | Russell, S. O., & Campbell, P. F. (1996). Reservoir Operating Rules with Fuzzy Programming. Journal of Water Resources Planning and Management, 122(3), 165-170. doi:10.1061/(asce)0733-9496(1996)122:3(165) | es_ES |
dc.description.references | Safavi, H. R., & Enteshari, S. (2016). Conjunctive use of surface and ground water resources using the ant system optimization. Agricultural Water Management, 173, 23-34. doi:10.1016/j.agwat.2016.05.001 | es_ES |
dc.description.references | Sahinidis, N. V. (2004). Optimization under uncertainty: state-of-the-art and opportunities. Computers & Chemical Engineering, 28(6-7), 971-983. doi:10.1016/j.compchemeng.2003.09.017 | es_ES |
dc.description.references | Zatarain Salazar, J., Reed, P. M., Herman, J. D., Giuliani, M., & Castelletti, A. (2016). A diagnostic assessment of evolutionary algorithms for multi-objective surface water reservoir control. Advances in Water Resources, 92, 172-185. doi:10.1016/j.advwatres.2016.04.006 | es_ES |
dc.description.references | Satti, S., Zaitchik, B., & Siddiqui, S. (2015). The question of Sudan: a hydro-economic optimization model for the Sudanese Blue Nile. Hydrology and Earth System Sciences, 19(5), 2275-2293. doi:10.5194/hess-19-2275-2015 | es_ES |
dc.description.references | kumar, A. R. S., Goyal, M. K., Ojha, C. S. P., Singh, R. D., Swamee, P. K., & Nema, R. K. (2012). Application of ANN, Fuzzy Logic and Decision Tree Algorithms for the Development of Reservoir Operating Rules. Water Resources Management, 27(3), 911-925. doi:10.1007/s11269-012-0225-8 | es_ES |
dc.description.references | Sheibani, H., Alizadeh, H., & Shourian, M. (2018). Optimum Design and Operation of a Reservoir and Irrigation Network Considering Uncertainty of Hydrologic, Agronomic and Economic Factors. Water Resources Management, 33(2), 863-879. doi:10.1007/s11269-018-2148-5 | es_ES |
dc.description.references | Sherafatpour, Z., Roozbahani, A., & Hasani, Y. (2019). Agricultural Water Allocation by Integration of Hydro-Economic Modeling with Bayesian Networks and Random Forest Approaches. Water Resources Management, 33(7), 2277-2299. doi:10.1007/s11269-019-02240-9 | es_ES |
dc.description.references | Shourian, M., Mousavi, S. J., & Tahershamsi, A. (2007). Basin-wide Water Resources Planning by Integrating PSO Algorithm and MODSIM. Water Resources Management, 22(10), 1347-1366. doi:10.1007/s11269-007-9229-1 | es_ES |
dc.description.references | Shrestha, B. P., Duckstein, L., & Stakhiv, E. Z. (1996). Fuzzy Rule-Based Modeling of Reservoir Operation. Journal of Water Resources Planning and Management, 122(4), 262-269. doi:10.1061/(asce)0733-9496(1996)122:4(262) | es_ES |
dc.description.references | Simonovic, S. P. (1992). Reservoir Systems Analysis: Closing Gap between Theory and Practice. Journal of Water Resources Planning and Management, 118(3), 262-280. doi:10.1061/(asce)0733-9496(1992)118:3(262) | es_ES |
dc.description.references | Singh, A. (2012). An overview of the optimization modelling applications. Journal of Hydrology, 466-467, 167-182. doi:10.1016/j.jhydrol.2012.08.004 | es_ES |
dc.description.references | Soleimani, S., Bozorg-Haddad, O., & Loáiciga, H. A. (2016). Reservoir Operation Rules with Uncertainties in Reservoir Inflow and Agricultural Demand Derived with Stochastic Dynamic Programming. Journal of Irrigation and Drainage Engineering, 142(11), 04016046. doi:10.1061/(asce)ir.1943-4774.0001065 | es_ES |
dc.description.references | Soleimani, S., Bozorg-Haddad, O., Saadatpour, M., & Loáiciga, H. A. (2016). Optimal Selective Withdrawal Rules Using a Coupled Data Mining Model and Genetic Algorithm. Journal of Water Resources Planning and Management, 142(12), 04016064. doi:10.1061/(asce)wr.1943-5452.0000717 | es_ES |
dc.description.references | Sordo-Ward, A., Garrote, L., Martín-Carrasco, F., & Dolores Bejarano, M. (2012). Extreme flood abatement in large dams with fixed-crest spillways. Journal of Hydrology, 466-467, 60-72. doi:10.1016/j.jhydrol.2012.08.009 | es_ES |
dc.description.references | Spiliotis, M., Mediero, L., & Garrote, L. (2016). Optimization of Hedging Rules for Reservoir Operation During Droughts Based on Particle Swarm Optimization. Water Resources Management, 30(15), 5759-5778. doi:10.1007/s11269-016-1285-y | es_ES |
dc.description.references | Sreekanth, J., Datta, B., & Mohapatra, P. K. (2012). Optimal Short-term Reservoir Operation with Integrated Long-term Goals. Water Resources Management, 26(10), 2833-2850. doi:10.1007/s11269-012-0051-z | es_ES |
dc.description.references | Srinivasan, K., & Kumar, K. (2018). Multi-Objective Simulation-Optimization Model for Long-term Reservoir Operation using Piecewise Linear Hedging Rule. Water Resources Management, 32(5), 1901-1911. doi:10.1007/s11269-018-1911-y | es_ES |
dc.description.references | Stedinger, J. R., Sule, B. F., & Loucks, D. P. (1984). Stochastic dynamic programming models for reservoir operation optimization. Water Resources Research, 20(11), 1499-1505. doi:10.1029/wr020i011p01499 | es_ES |
dc.description.references | Sun, X., Ma, C., & Lian, J. (2018). Optimal Operation of Danjiangkou Reservoir Using Improved Hedging Model and Considering the Effects of Historical Decisions. Journal of Water Resources Planning and Management, 144(1), 04017080. doi:10.1061/(asce)wr.1943-5452.0000868 | es_ES |
dc.description.references | Tan, Q., Wang, X., Wang, H., Wang, C., Lei, X., Xiong, Y., & Zhang, W. (2017). Derivation of optimal joint operating rules for multi-purpose multi-reservoir water-supply system. Journal of Hydrology, 551, 253-264. doi:10.1016/j.jhydrol.2017.06.009 | es_ES |
dc.description.references | Taormina, R., Chau, K.-W., & Sivakumar, B. (2015). Neural network river forecasting through baseflow separation and binary-coded swarm optimization. Journal of Hydrology, 529, 1788-1797. doi:10.1016/j.jhydrol.2015.08.008 | es_ES |
dc.description.references | Teegavarapu, R. S. V., & Simonovic, S. P. (2000). Short-Term Operation Model for Coupled Hydropower Reservoirs. Journal of Water Resources Planning and Management, 126(2), 98-106. doi:10.1061/(asce)0733-9496(2000)126:2(98) | es_ES |
dc.description.references | Teegavarapu, R. S. V., & Simonovic, S. P. (2002). Water Resources Management, 16(5), 401-428. doi:10.1023/a:1021993222371 | es_ES |
dc.description.references | Tejada-Guibert, J. A., Johnson, S. A., & Stedinger, J. R. (1993). Comparison of two approaches for implementing multireservoir operating policies derived using stochastic dynamic programming. Water Resources Research, 29(12), 3969-3980. doi:10.1029/93wr02277 | es_ES |
dc.description.references | Tilmant, A., Faouzi, E. H., & Vanclooster, M. (2002). Optimal operation of multipurpose reservoirs using flexible stochastic dynamic programming. Applied Soft Computing, 2(1), 61-74. doi:10.1016/s1568-4946(02)00029-7 | es_ES |
dc.description.references | Tilmant, A., & Kelman, R. (2007). A stochastic approach to analyze trade-offs and risks associated with large-scale water resources systems. Water Resources Research, 43(6). doi:10.1029/2006wr005094 | es_ES |
dc.description.references | Tilmant, A., Pinte, D., & Goor, Q. (2008). Assessing marginal water values in multipurpose multireservoir systems via stochastic programming. Water Resources Research, 44(12). doi:10.1029/2008wr007024 | es_ES |
dc.description.references | Tilmant, A., van der Zaag, P., & Fortemps, P. (2007). Modeling and analysis of collective management of water resources. Hydrology and Earth System Sciences, 11(2), 711-720. doi:10.5194/hess-11-711-2007 | es_ES |
dc.description.references | Tilmant, A., Vanclooster, M., Duckstein, L., & Persoons, E. (2002). Comparison of Fuzzy and Nonfuzzy Optimal Reservoir Operating Policies. Journal of Water Resources Planning and Management, 128(6), 390-398. doi:10.1061/(asce)0733-9496(2002)128:6(390) | es_ES |
dc.description.references | Vedula, S., Mujumdar, P. P., & Chandra Sekhar, G. (2005). Conjunctive use modeling for multicrop irrigation. Agricultural Water Management, 73(3), 193-221. doi:10.1016/j.agwat.2004.10.014 | es_ES |
dc.description.references | Vermuyten, E., Meert, P., Wolfs, V., & Willems, P. (2018). Combining Model Predictive Control with a Reduced Genetic Algorithm for Real-Time Flood Control. Journal of Water Resources Planning and Management, 144(2), 04017083. doi:10.1061/(asce)wr.1943-5452.0000859 | es_ES |
dc.description.references | Vieira, J., Cunha, M. C., Nunes, L., Monteiro, J. P., Ribeiro, L., Stigter, T., … Lucas, H. (2011). Optimization of the Operation of Large-Scale Multisource Water-Supply Systems. Journal of Water Resources Planning and Management, 137(2), 150-161. doi:10.1061/(asce)wr.1943-5452.0000102 | es_ES |
dc.description.references | Wan, W., Guo, X., Lei, X., Jiang, Y., & Wang, H. (2017). A Novel Optimization Method for Multi-Reservoir Operation Policy Derivation in Complex Inter-Basin Water Transfer System. Water Resources Management, 32(1), 31-51. doi:10.1007/s11269-017-1735-1 | es_ES |
dc.description.references | Wan, W., Zhao, J., Lund, J. R., Zhao, T., Lei, X., & Wang, H. (2016). Optimal Hedging Rule for Reservoir Refill. Journal of Water Resources Planning and Management, 142(11), 04016051. doi:10.1061/(asce)wr.1943-5452.0000692 | es_ES |
dc.description.references | Wei, C.-C., & Hsu, N.-S. (2008). Derived operating rules for a reservoir operation system: Comparison of decision trees, neural decision trees and fuzzy decision trees. Water Resources Research, 44(2). doi:10.1029/2006wr005792 | es_ES |
dc.description.references | Wild, T. B., Reed, P. M., Loucks, D. P., Mallen-Cooper, M., & Jensen, E. D. (2019). Balancing Hydropower Development and Ecological Impacts in the Mekong: Tradeoffs for Sambor Mega Dam. Journal of Water Resources Planning and Management, 145(2), 05018019. doi:10.1061/(asce)wr.1943-5452.0001036 | es_ES |
dc.description.references | Wurbs, R. A. (1993). Reservoir‐System Simulation and Optimization Models. Journal of Water Resources Planning and Management, 119(4), 455-472. doi:10.1061/(asce)0733-9496(1993)119:4(455) | es_ES |
dc.description.references | Yang, P., & Ng, T. L. (2017). Fuzzy Inference System for Robust Rule-Based Reservoir Operation under Nonstationary Inflows. Journal of Water Resources Planning and Management, 143(4), 04016084. doi:10.1061/(asce)wr.1943-5452.0000743 | es_ES |
dc.description.references | Yang, T., Gao, X., Sorooshian, S., & Li, X. (2016). Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme. Water Resources Research, 52(3), 1626-1651. doi:10.1002/2015wr017394 | es_ES |
dc.description.references | Yeh, W. W.-G. (1985). Reservoir Management and Operations Models: A State-of-the-Art Review. Water Resources Research, 21(12), 1797-1818. doi:10.1029/wr021i012p01797 | es_ES |
dc.description.references | You, J.-Y., & Cai, X. (2008). Determining forecast and decision horizons for reservoir operations under hedging policies. Water Resources Research, 44(11). doi:10.1029/2008wr006978 | es_ES |
dc.description.references | Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-x | es_ES |
dc.description.references | Zeff, H. B., Kasprzyk, J. R., Herman, J. D., Reed, P. M., & Characklis, G. W. (2014). Navigating financial and supply reliability tradeoffs in regional drought management portfolios. Water Resources Research, 50(6), 4906-4923. doi:10.1002/2013wr015126 | es_ES |
dc.description.references | Zhang, X., Liu, P., Xu, C.-Y., Guo, S., Gong, Y., & Li, H. (2019). Derivation of Hydropower Rules for Multireservoir Systems and Its Application for Optimal Reservoir Storage Allocation. Journal of Water Resources Planning and Management, 145(5), 04019010. doi:10.1061/(asce)wr.1943-5452.0001056 | es_ES |
dc.description.references | Zhu, X., Zhang, C., Yin, J., Zhou, H., & Jiang, Y. (2014). Optimization of Water Diversion Based on Reservoir Operating Rules: Analysis of the Biliu River Reservoir, China. Journal of Hydrologic Engineering, 19(2), 411-421. doi:10.1061/(asce)he.1943-5584.0000805 | es_ES |
dc.description.references | Zubaidi, S. L., Gharghan, S. K., Dooley, J., Alkhaddar, R. M., & Abdellatif, M. (2018). Short-Term Urban Water Demand Prediction Considering Weather Factors. Water Resources Management, 32(14), 4527-4542. doi:10.1007/s11269-018-2061-y | es_ES |
dc.subject.ods | 11.- Conseguir que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles | es_ES |