Document Type : Research Paper

Authors

1 Department of mathematics, university of Mazandaran, Babolsar ,Iran

2 Department of Mathematical Sciences, Mazandaran University, Babolsar, Iran

Abstract

Irrigation water management is crucial for agricultural production and livelihood security in many regions and countries throughout the world. Over the past decades, controversial and conflictladen water-allocation issues among competing municipal, industrial and agricultural interests have raised increasing concerns. Particularly, growing population, varying natural conditions and shrinking water availabilities have exacerbated such competitions. Shrinking water availabilities can result in reduced water supplies, while growing population can lead to increased water demands, these two facts can further intensify the water shortage. Stochastic programming methodology is applied in this paper to a capital investment problem in water resources. A framework is offered for the evaluation of electricity generation and water supply for agricultural irrigation. This essessment is conducted through the construction of an appropriate stochastic optimization model. A recursive least squares algorithm is incorp-orated in the model which enablee more accurate estimation of model parameters.

Keywords

Main Subjects

Arunkumar, S., & Yeh, W. G. (1973). Probabilistic models in the design and operation of a multi-purpose reservoir system. AVAILABLE FROM THE NATIONAL TECHNICAL INFORMATION SERVICE AS PB-232 410, 3(25).
Askew, A. J. (1974). Chance‐constrained dynamic programing and the optimization of water resource systems. Water Resources Research, 10(6), 1099-1106.
Askew, A. J. (1974). Optimum reservoir operating policies and the imposition of a reliability constraint. Water Resources Research, 10(1), 51-56.
Butcher, W. S. (1971). Stochastic dynamic programming for optimum reservoir operation. JAWRA Journal of the American Water Resources Association, 7(1), 115-123.
Charnes, A., & Cooper, W. W. (1963). Deterministic equivalents for optimizing and satisficing under chance constraints. Operations research, 11(1), 18-39.
Geib, A.(1974). Editor, Applied OptGnal Estirnution, M.I.T. Press, Cambridge.
Li, Y. P., Huang, G. H., & Nie, S. L. (2006). An interval-parameter multi-stage stochastic programming model for water resources management under uncertainty. Advances in Water Resources, 29(5), 776-789.
Li, Z., Deng, X., Wu, F., & Hasan, S. S. (2015). Scenario analysis for water resources in response to land use change in the middle and upper reaches of the Heihe River Basin. Sustainability, 7(3), 3086-3108.
Ma, Z. Z., Wang, Z. J., Xia, T., Gippel, C. J., & Speed, R. (2014). Hydrograph-Based Hydrologic Alteration Assessment and Its Application to the Yellow River. Journal of Environmental Informatics, 23(1).
Maqsood, I., Huang, G. H., & Yeomans, J. S. (2005). An interval-parameter fuzzy two-stage stochastic program for water resources management under uncertainty. European Journal of Operational Research, 167(1), 208-225.
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.
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.
Taylor, H. M., & Karlin, S. (1984). An introduction to stochastic modeling Academic Press. New York.
Trezos, T., & Yeh, W. W. G. (1987). Use of stochastic dynamic programming for reservoir management. Water Resources Research, 23(6), 983-996.
Wang, S., & Huang, G. H. (2011). Interactive two-stage stochastic fuzzy programming for water resources management. Journal of environmental management, 92(8), 1986-1995.
Wang, Z., Yang, J., Deng, X., & Lan, X. (2015). Optimal water resources allocation under the constraint of land use in the Heihe River Basin of China. Sustainability, 7(2), 1558-1575.
Yeh, W. W. G. (1985). Reservoir management and operations models: A state‐of‐the‐art review. resources research, 21(12), 1797-1818.