Journal ArticleDOI
Multireservoir Modeling with Dynamic Programming and Neural Networks
Reads0
Chats0
TLDR
The multireservoir model based on the dynamic programming-neural network algorithm gives improved performance in this study.Abstract:
For optimal multireservoir operation, a dynamic programming-based neural network model is developed in this study. In the suggested model, multireservoir operating rules are derived using a feedforward neural network from the results of three state variables' dynamic programming algorithm. The training of the neural network is done using a supervised learning approach with the back-propagation algorithm. A multireservoir system called the Parambikulam Aliyar Project system is used for this study. The performance of the new multireservoir model is compared with (1) the regression-based approach used for deriving the multireservoir operating rules from optimization results; and (2) the single-reservoir dynamic programming-neural network model approach. The multireservoir model based on the dynamic programming-neural network algorithm gives improved performance in this study.read more
Citations
More filters
Journal ArticleDOI
Optimal Operation of Multireservoir Systems: State-of-the-Art Review
TL;DR: Application of heuristic programming methods using evolutionary and genetic algorithms are described, along with application of neural networks and fuzzy rule-based systems for inferring reservoir system operating rules, to assess the state of the art in optimization of reservoir system management and operations.
Journal ArticleDOI
Simulation-optimization modeling: a survey and potential application in reservoir systems operation.
TL;DR: Simulation, optimization and combined simulation–optimization modeling approach are discussed and an overview of their applications reported in literature is provided to help system managers decide appropriate methodology for application to their systems.
Journal ArticleDOI
Influence of uncertain boundary conditions and model structure on flood inundation predictions.
Florian Pappenberger,Patrick Matgen,Keith Beven,J.-B. Henry,Laurent Pfister,Paul de Fraipont +5 more
TL;DR: Uncertainty of the upstream boundary can have significant impact on the model results, exceeding the importance of model parameter uncertainty in some areas, however, this depends on the hydraulic conditions in the reach e.g. internal boundary conditions and, for example, the amount of backwater within the modelled region.
Journal ArticleDOI
An overview of the optimization modelling applications
TL;DR: The comprehensive reviews on the use of various programming techniques for the solution of different optimization problems have been provided and conclusions are drawn where gaps exist and more research needs to be focused.
Journal ArticleDOI
Evaluation of stochastic reservoir operation optimization models
Alcigeimes B. Celeste,Max Billib +1 more
TL;DR: The proposed ISO-based surface modeling procedure and the PSO-based two-dimensional hedging rule showed superior overall performance as compared with the neuro-fuzzy approach.