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Inferring efficient operating rules in multireservoir water resource systems: A review

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Inferring efficient operating rules in multireservoir water resource systems: A review

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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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/140500

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Título: Inferring efficient operating rules in multireservoir water resource systems: A review
Autor: Macian-Sorribes, Hector Pulido-Velazquez, M.
Entidad UPV: Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient
Fecha difusión:
Resumen:
[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 ...[+]
Palabras clave: Optimization , Reservoir operation , Stochastic programming , Water resources management
Derechos de uso: Reserva de todos los derechos
Fuente:
Wiley Interdisciplinary Reviews Water. (issn: 2049-1948 )
DOI: 10.1002/wat2.1400
Editorial:
John Wiley & Sons
Versión del editor: https://doi.org/10.1002/wat2.1400
Código del Proyecto:
info:eu-repo/grantAgreement/UPV//PAID-10-18/
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/
Agradecimientos:
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 ...[+]
Tipo: Artículo

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

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

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 [+]
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

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

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

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

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)

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

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

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

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

Bellman, R. E., & Dreyfus, S. E. (1962). Applied Dynamic Programming. doi:10.1515/9781400874651

Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust Optimization. doi:10.1515/9781400831050

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)

Bhaskar, N. R., & Whitlatch, E. E. (1980). Derivation of monthly reservoir release policies. Water Resources Research, 16(6), 987-993. doi:10.1029/wr016i006p00987

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

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

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

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

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

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

Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324

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

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

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)

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

Cancelliere, A., Giuliano, G., Ancarani, A., & Rossi, G. (2002). Water Resources Management, 16(1), 71-88. doi:10.1023/a:1015563820136

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

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

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

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

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

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

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

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

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)

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

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

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

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

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

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

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

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

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)

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

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

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

Eisel, L. M. (1972). Chance constrained reservoir model. Water Resources Research, 8(2), 339-347. doi:10.1029/wr008i002p00339

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.

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.

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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)

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

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

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)

Labadie J. W. Baldo M. &Larson R.(2000).MODSIM: Decision support system for river basin management. Documentation and user manual.

Lee, J.-H., & Labadie, J. W. (2007). Stochastic optimization of multireservoir systems via reinforcement learning. Water Resources Research, 43(11). doi:10.1029/2006wr005627

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

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

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

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

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

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

Loucks, D. P. (1970). Some Comments on Linear Decision Rules and Chance Constraints. Water Resources Research, 6(2), 668-671. doi:10.1029/wr006i002p00668

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

Lund J. R.(1996).Developing seasonal and long‐term reservoir system operation plans using HEC‐PRM Davis CA.

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)

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)

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

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.

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

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

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

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

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

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

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

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

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

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

Panigrahi, D. P., & Mujumdar, P. P. (2000). Water Resources Management, 14(2), 89-109. doi:10.1023/a:1008170632582

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

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

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

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)

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

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

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

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

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

Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81-106. doi:10.1007/bf00116251

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

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

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

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

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

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

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

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)

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

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

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

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

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

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

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

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

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)

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)

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

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

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

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

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

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

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

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

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

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

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

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)

Teegavarapu, R. S. V., & Simonovic, S. P. (2002). Water Resources Management, 16(5), 401-428. doi:10.1023/a:1021993222371

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

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

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

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

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

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)

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

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

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

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

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

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

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

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)

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

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

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

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

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-x

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

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

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

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

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