TY - JFULL AU - M. Boudjerda and B. Touaibia and M. K. Mihoubi PY - 2020/6/ TI - Optimization of Agricultural Water Demand Using a Hybrid Model of Dynamic Programming and Neural Networks: A Case Study of Algeria T2 - International Journal of Environmental and Ecological Engineering SP - 120 EP - 124 VL - 14 SN - 1307-6892 UR - https://publications.waset.org/pdf/10011223 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 161, 2020 N2 - 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. ER -