Use of developed GP Optimization Tool for Multi-objective Operating of Reservoirs in Climate Change Conditions

Document Type : Research Paper

Authors

1 Ph. D. Candidate, University College of Agriculture and Natural Resources, University of Tehran

2 Associate Professor, University College of Agriculture and Natural Resources, University of Tehran

Abstract

The application of optimization methods and tools for multi-objective utilization, in operation of a reservoir in the wake of climate change conditions is an inevitable issue. In this study, Multi-Objective Genetic Programming (MO-GP) is employed to extract multi-objective optimal operating rules from Aidoghmoush reservoir (East Azerbaijan) in climate change conditions. These rules are derived with two objectives of minimization of the vulnerability and maximization of the reliability in the baseline (interval 1987-2000) and climate change (interval 2026-2039) conditions. The results show that the range of changes of the vulnerability index in the baseline vs climate change conditions are from 16 to 41% and from 11 to 35% and the range of changes of the reliability index in the baseline vs climate change conditions are from 46 to 78% and 30 to 77%. In order to do more investigations, the two alternatives (development of rules in the baseline operating interval as based upon the baseline conditions; and rules developed within climate change operating intervals as based upon climate change conditions) are considered. In order to investigate the performance of the reservoir in supplying of the demand, the objective function values for a Pareto point (reliability of 75%) in the two alternatives under consideration are compared. The results show that the second alternative is of a more appropriate performance, relative to the first one.

Keywords

Main Subjects

References

Ashofteh, P. S., Bozorg Haddad, O., and Mariño, M. A. (2013a). “Climate change impact on reservoir performance indices in agricultural water supply”,Journal of Irrigation and Drainage Engineering, 139 (2), 85-97.
Ashofteh, P. S., Bozorg Haddad, O., and Mariño, M. A. (2013b). “Scenario assessment of streamflow simulation and its transition probability in future periods under climate change”,Water Resources Management, 27 (1), 255-274.
Ashofteh, P. S., Bozorg Haddad, O., Akbari-Alashti, H., and Mariño, M. A. (2014). “Determination of irrigation allocation policy under climate change by genetic programming”,Journal of Hydrologic Engineering, doi: 10.1061/(ASCE)IR.1943-4774.0000807, 04014059.
Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T. (2000). “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II”,Lecture notes in computer science, In Proceedings of Parallel Problem Solving from Nature PPSN VI, Paris, France, September 16-20, pp. 849-858.
Jakeman A. J. and Hornberger G. M. (1993).“How much complexity is warranted in a rainfall-runoff model?”,Water Resources Research, 29 (8), 2637-2649.
Raje, D. and Mujumdar, P. P. (2010). “Reservoir performance under uncertainty in hydrologic impacts of climate change”, Advances in Water Resources, 33 (3), 312-326.
Reddy, M. J. and Kumar, D. N. (2008). “Evolving strategies for crop planning and operation of irrigation reservoir system using multi-objective differential evolution”,Irrigation Science, 26 (2), 177-190.
Rezapour Tabari, M. M. and Soltani, J. (2012). “Multi-objective optimal model for conjunctive use management using SGAs and NSGA-II models”,Water Resources Management, 27 (1), 37-53.
Silva, S. (2007). “GPLAB: A genetic programming toolbox for Matlab, Version 3”, ECOS-Evolutionary and Complex Systems Group, University of Coimbra, Portugal, pp. 13-15.
Sivapragasam, C., Mahewaran, R., and Venkatesh, V. (2008). “Genetic programming approach for flood routing in natural channels”, Hydrological Processes, 25 (5), 623-628.
Sivapragasam, C., Vasudevan, G., Maran, J., Bose, C., Kaza, S., and Ganesh, N. (2009). “Modeling evaporation-seepage losses for reservoir water balance in semi-arid regions”, Water Resources Management, 23 (5), 853-867.
Wang, W. C., Chau, K. W., Cheng, C. T., and Qiu, L. (2009). “A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series”, Journal of Hydrology, 374 (3-4), 294-306.
Wilby, R. L. and Harris, I. (2006). “A framework for assessing uncertainties in climate change impacts: Low-flow scenarios for the river Thames, UK”,Water Resources Research, 42 (2), W02419.
Yang, C. Ch., Chang, L. Ch., Chen, Ch. Sh., and Yeh, M. Sh. (2009). “Multi-objective planning for conjunctive use of surface and subsurface water using genetic algorithm and dynamics programming”,Water Resources Management, 23 (23), 417-437.
Iranian Journal of Soil and Water Research
Volume 46, Issue 3 - Serial Number 3
October 2015
Pages 415-422

Files

Share

How to cite

Statistics