Document Type: Regular Article
Department of Civil and Environmental Engineering,
University of Washington.
University of North Dakota, United States.
Department of Mechanical Engineering, The Polytechnic Ibadan, Nigeria.
Reservoir operations need computational models that can attend to both its real time data analytics and multi-objective optimization. This is now increasingly necessary due to the growing complexities of reservoir’s hydrological structures, ever-increasing its operational data, and conflicting conditions in optimizing the its operations. Past related studies have mostly attended to either real time data analytics, or multi-objective optimization of reservoir operations. This review study, based on systematic literature analysis, presents the suitability of Recurrent Learning Neural Network (RLNN) and Combine Pareto Multi-objective Differential Evolution (CPMDE) algorithms for real time data analytics and multi-objective optimization of reservoir operations, respectively. It also presents the need for a hybrid RLNN-CPMDE, with the use of CPMDE in the development of RLNN learning data, for reservoir operation optimization in a multi-objective and real time environment. This review is necessary as a reference for researchers in multi-objective optimization and reservoir real time operations. The gaps in research reported in this review would be areas of further studies in real time multi-objective studies in reservoir operation.