Open Access
Issue
MATEC Web Conf.
Volume 136, 2017
2017 2nd International Conference on Design, Mechanical and Material Engineering (D2ME 2017)
Article Number 02010
Number of page(s) 7
Section Chapter 2: Design
DOI https://doi.org/10.1051/matecconf/201713602010
Published online 14 November 2017
  1. Hejazi,M. & Cai,X. Building more realistic reservoir optimization models using data mining–A case study of Shelbyville Reservoir. Adv. W. Resour. 34,701–717 (2011). [CrossRef] [Google Scholar]
  2. Sniedovich,M. Reliability‐constrained reservoir control problems: 1. Methodological issues. Water Resour. Res. 15(6), 1574–1582. (1979). [CrossRef] [Google Scholar]
  3. Su,S. & Deininger,R. Modeling the regulation of Lake Superior under uncertainty of future water supplies. Water Resour. Res. 10(1), 11–25 (1974). [CrossRef] [Google Scholar]
  4. Mousavi,S. & Karamouz,M. Fuzzy-state stochastic dynamic programming for reservoir operation. J. water Resour. 130(6), 460–470 (2004). [Google Scholar]
  5. Huang,W. & Yuan,L. A drought early warning system on real‐time multireservoir operations. Water Resour. Res. 40(6) (2004). [Google Scholar]
  6. Ganji,A., Karamouz,M. & Khalili,D. Development of stochastic dynamic Nash game model for reservoir operation II. The value of players’ information availability and cooperative behaviors. Adv. Water Resour. 30(3), 528–542 (2007). [CrossRef] [Google Scholar]
  7. Kucukmehmetoglu,M. A game theoretic approach to assess the impacts of major investments on transboundary water resources: the case of the Euphrates and Tigris. Water Resour. Manag. 23(15), 3069–3099 (2009). [CrossRef] [Google Scholar]
  8. Goldberg,D. Genetic algorithms in search, optimization, and machine learning, 1989. Read. Addison-Wesley (1989). [Google Scholar]
  9. Chang,F., Chen,L. & Chang,L. Optimizing the reservoir operating rule curves by genetic algorithms. Hydrol. Process. 19(11), 2277–2289 (2005). [CrossRef] [Google Scholar]
  10. Jian-Xia,C., Qiang,H. & Yi-Min,W. Genetic algorithms for optimal reservoir dispatching. Water Resour. Manag. 19(4), 321–331 (2005). [CrossRef] [Google Scholar]
  11. Oliveira,R. & Loucks,D. Operating rules for multireservoir systems. Water Resour. Res. 33(4), 839–852 (1997). [CrossRef] [Google Scholar]
  12. Wardlaw,R. & Sharif,M. Evaluation of genetic algorithms for optimal reservoir system operation. J. water Resour. Plan. 125(1), 25–33 (1999). [CrossRef] [Google Scholar]
  13. Sharif,M. & Wardlaw,R. Multireservoir systems optimization using genetic algorithms: case study. J. Comput. Civ. Eng. 14(4), 255–263 (2000). [CrossRef] [EDP Sciences] [Google Scholar]
  14. Zyl,J. Van, Savic,D. & Walters,G. Operational optimization of water distribution systems using a hybrid genetic algorithm. J. water Resour. 130(2), 160–170 (2004). [Google Scholar]
  15. Wang,K., Chang,L. & Chang,F. Multi-tier interactive genetic algorithms for the optimization of long-term reservoir operation. Adv. Water Resour. 34(10), 1343–1351 (2011). [CrossRef] [Google Scholar]
  16. Kennedy,J. & Eberhart,R. Particle swarm optimization. 1995. Proceedings., IEEE Int.pp. 39–43 (1995). [Google Scholar]
  17. Rahi,O., Chandel,A. & Sharma,M. Optimization of hydro power plant design by particle swarm optimization (PSO). Procedia Eng. 30, 418–425 (2012). [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.