Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models

Abstract

Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.

State-of-the Art-Powerhouse, Dam Structure, and Turbine Operation and Vibrations

“…This, in turn, helps policymakers, engineers, and project managers to manage and operate the system more efficiently. Many AI models have been applied to the modeling and operation of dams and reservoirs considering different stochastic hydrological parameters [241]. Many AI models have been employed; however, faster problem-solving models are required along with better optimizers, including methods that can select the input and manage non-linear data and more experimental data to develop an effective model.…”

Psychosocial Risks Assessment in Cryopreservation Laboratories

“…ANNs are increasingly applied in data mining because of their good performance in prediction [ 13 ]. In last decades, several studies have been published showing the usefulness of ANNs to apprehend complex relationships between variables, in various areas of application (e.g., environment [ 14 , 15 ], health [ 16 , 17 ], and law [ [18] , [19] , [20] ] just to name a few.…”

Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation: case study of Nasser Lake in Egypt

“…ANFIS employs a hybrid framework that combines backpropagation and least-squares approaches to provide the fuzzy 'if-then' rules. This framework, termed the neurofuzzy system, includes the integration of the ANN and the fuzzy model, where a multilayer feed-forward network is developed by employing neural network learning models and the fuzzy logic framework to map the input space into the output space (Allawi, Jaafar, Mohamad Hamzah, Abdullah, & El-shafie, 2018). The common rules with two-fuzzy (i.e.…”