Optimization of Agricultural Water Demand Using a Hybrid Model of Dynamic Programming and Neural Networks: A Case Study of Algeria
Abstract:In Algeria agricultural irrigation is the primary water consuming sector followed by the domestic and industrial sectors. Economic development in the last decade has weighed heavily on water resources which are relatively limited and gradually decreasing to the detriment of agriculture. The research presented in this paper focuses on the optimization of irrigation water demand. Dynamic Programming-Neural Network (DPNN) method is applied to investigate reservoir optimization. The optimal operation rule is formulated to minimize the gap between water release and water irrigation demand. As a case study, Foum El-Gherza dam’s reservoir system in south of Algeria has been selected to examine our proposed optimization model. The application of DPNN method allowed increasing the satisfaction rate (SR) from 12.32% to 55%. In addition, the operation rule generated showed more reliable and resilience operation for the examined case study.
 P. Mujumdar and T. Ramesh S. V, “Real-time reservoir operation for irrigation,” Water Resources Research, vol. 33, no. 5, pp.1157-1164, 1997.
 F. Lebdi, M. Slimani, and E. Parent, “Empirical strategy for water resources system management: the example of the semi-arid irrigated perimeter,” Rev Sci Eau. 1. Pp.121-134, 1997.
 A. Ben Alaya, A. Souissi. J. Tarhouni and K. Ncib, “Optimization of Nebhana Reservoir Water Allocation by Stochastic Dynamic Programming,” Water Resources Management, 17: 259–272. 2003.
 K. Suresh, and P. Mujumdar, “A fuzzy risk approach for performance evaluation of an irrigation reservoir system,” Agricultural Water Management, 69, pp159–177. 2004.
 A. Elferchichi, O. Gharsallah, I. Nouiri, F. Lebdi, and N. Lamaddalena, “The genetic algorithm approach for identifying the optimal operation of a multi-reservoirs on-demand irrigation system,” Bio systems engineering, 102. 334–344. 2009.
 Y. Wu and J. Chenb, “Estimating irrigation water demand using an improved method and optimizing reservoir operation for water supply and hydropower generation: A case study of the Xinfengjiang reservoir in southern China,” Agricultural Water Management, 116, pp110– 121. 2013.
 S. Salvi, “Optimization Reservoir Operation for Irrigation Using Fuzzy Logic of Jayakwadi Dam,” International Journal of Science and Research (IJSR), Volume 6, Issue 7. 2015.
 S. Vedul, P. Mujumdar, “Water resources systems: Modeling techniques and analysis. PP 279, Tata McGraw-Hill. 2005.
 H. Chandramouli, and H. Raman, “Multireservoir modeling with dynamic programming and neural networks,” ASCE J. Water Resour. Plann. Manage. 127 (2): 89–98. 2001.
 A. Cancelliere, G. Giuliano, Ancarani A, and G. Rossi, “A neural networks approach for deriving irrigation reservoir operating rules,” Water Resour. Manage. 16(1), 71–88. 2002.
 A. Shahidi, “Evaluation of combined model of DP and Neural Networks in single reservoir operation,” Journal of Applied Sciences Research, 5(10):1307-1312.2009.
 A. Safayat, “Optimal Operation of Single Reservoir Using Artificial Neural Network,” International Journal of Civil Engineering and Technology (IJCIET), Volume 6, Issue 6, pp. 124-132, 2015.