Articles | Volume 19, issue 8
https://doi.org/10.5194/hess-19-3557-2015
https://doi.org/10.5194/hess-19-3557-2015
Research article
 | 
12 Aug 2015
Research article |  | 12 Aug 2015

Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

J. Chu, C. Zhang, G. Fu, Y. Li, and H. Zhou

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Cited articles

Castelletti, A., Pianosi, F., Quach, X., and Soncini-Sessa, R.: Assessing water reservoirs management and development in Northern Vietnam, Hydro l. Earth Syst. Sci., 16, 189–199, https://doi.org/10.5194/hess-16-189-2012, 2012.
Celeste, A. B. and Billib, M.: Evaluation of stochastic reservoir operation optimization models, Adv. Water Resour., 32, 1429–1443, 2009.
Chang, F. J., Chen, L., and Chang, L. C.: Optimizing the reservoir operating rule curves by genetic algorithms, Hydrol. Process., 19, 2277–2289, 2005.
Chang, J. X., Bai, T., Huang, Q., and Yang, D. W.: Optimization of water resources utilization by PSO-GA, Water Resour. Manag., 27, 3525–3540, 2013.
Chen, L.: Real coded genetic algorithm optimization of long term reservoir operation, J. Am. Water Resour. As., 39, 1157–1165, 2003.
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Short summary
This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses a global sensitivity analysis method to screen out insensitive decision variables and thus forms simplified problems with a significantly reduced number of decision variables. We find that it is important to consider variable interactions when formulating simplified problems, and problem decomposition dramatically improves search efficiency and effectiveness.