%0 Journal Article %T The Need for Recurrent Learning Neural Network and Combine Pareto Differential Algorithm for Multi-Objective Optimization of Real Time Reservoir Operations %J Journal of Soft Computing in Civil Engineering %I Pouyan Press %Z 2588-2872 %A Ajala, Abiodun Ladanu %A Adeyemo, Josiah %A Akanmu, Semiu %D 2020 %\ 07/01/2020 %V 4 %N 3 %P 52-64 %! The Need for Recurrent Learning Neural Network and Combine Pareto Differential Algorithm for Multi-Objective Optimization of Real Time Reservoir Operations %K Multi-Objective Optimization %K reservoir operations %K Real time recurrent learning Neural Network %K Pareto %K Differential evolution %R 10.22115/scce.2020.226578.1204 %X 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. %U https://www.jsoftcivil.com/article_110944_7578923b4457306a305f35c90a57d5ad.pdf