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Journal ArticleDOI

Multireservoir Modeling with Dynamic Programming and Neural Networks

TL;DR: 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.
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Journal ArticleDOI
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.
Abstract: With construction of new large-scale water storage projects on the wane in the U.S. and other developed countries, attention must focus on improving the operational effectiveness and efficiency of existing reservoir systems for maximizing the beneficial uses of these projects. Optimal coordination of the many facets of reservoir systems requires the assistance of computer modeling tools to provide information for rational management and operational decisions. The purpose of this review is to assess the state-of-the-art in optimization of reservoir system management and operations and consider future directions for additional research and application. Optimization methods designed to prevail over the high-dimensional, dynamic, nonlinear, and stochastic characteristics of reservoir systems are scrutinized, as well as extensions into multiobjective optimization. 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.

1,484 citations

Journal ArticleDOI
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.
Abstract: This paper presents a survey of simulation and optimization modeling approaches used in reservoir systems operation problems. Optimization methods have been proved of much importance when used with simulation modeling and the two approaches when combined give the best results. The main objective of this review article is to discuss simulation, optimization and combined simulation–optimization modeling approach and to provide an overview of their applications reported in literature. In addition to classical optimization techniques, application and scope of computational intelligence techniques, such as, evolutionary computations, fuzzy set theory and artificial neural networks, in reservoir system operation studies are reviewed. Conclusions and suggestive remarks based on this survey are outlined, which could be helpful for future research and for system managers to decide appropriate methodology for application to their systems.

428 citations

Journal ArticleDOI
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.

345 citations


Cites methods from "Multireservoir Modeling with Dynami..."

  • ...Other approaches include the use of weir equations [39]; theoretical relationships derived from flume experiments [57,58]; neural networks [59–63]; or M5 regression trees, which approximate the data by a set of linear equations [63]....

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Journal ArticleDOI
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.

194 citations

Journal ArticleDOI
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.

179 citations

References
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Journal ArticleDOI
TL;DR: The objective of this paper is to review the state-of-the-art of mathematical models developed for reservoir operations, including simulation, which include linear programming, dynamic programming, nonliner programming, and simulation.
Abstract: The objective of this paper is to review the state-of-the-art of mathematical models developed for reservoir operations, including simulation. Algorithms and methods surveyed include linear programming (LP), dynamic programming (DP), nonliner programming (NLP), and simulation. A general overview is first presented. The historical development of each key model is critically reviewed. Conclusions and recommendations for future research are presented.

1,345 citations

Journal ArticleDOI
TL;DR: A neural network is developed to forecast rainfall intensity fields in space and time using a three-layer learning network with input, hidden, and output layers and is shown to perform well when a relatively large number of hidden nodes are utilized.

675 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a survey of dynamic programming models for water resource problems and examine computational techniques which have been used to obtain solutions to these problems, including aqueduct design, irrigation system control, project development, water quality maintenance, and reservoir operations analysis.
Abstract: The central intention of this survey is to review dynamic programming models for water resource problems and to examine computational techniques which have been used to obtain solutions to these problems. Problem areas surveyed here include aqueduct design, irrigation system control, project development, water quality maintenance, and reservoir operations analysis. Computational considerations impose severe limitation on the scale of dynamic programming problems which can be solved. Inventive numerical techniques for implementing dynamic programming have been applied to water resource problems. Discrete dynamic programming, differential dynamic programming, state incremental dynamic programming, and Howard's policy iteration method are among the techniques reviewed. Attempts have been made to delineate the successful applications, and speculative ideas are offered toward attacking problems which have not been solved satisfactorily.

524 citations

Journal ArticleDOI
TL;DR: A broad array of computer models have been developed for evaluating reservoir operations as discussed by the authors, and selecting a modeling and analysis approach for a particular application depends upon the characteristics of the reservoir characteristics.
Abstract: A broad array of computer models has been developed for evaluating reservoir operations. Selecting a modeling and analysis approach for a particular application depends upon the characteristics of ...

494 citations

Journal ArticleDOI
TL;DR: In this article, a back-propagation neural network is trained to predict the peak discharge and the time of peak resulting from a single rainfall pattern, and the neural network was trained to map a time series of three rainfall patterns into a continuum of discharges over future time by using a discrete Fourier series fit to the runoff hydrograph.
Abstract: Spatially distributed rainfall patterns can now be detected using a variety of remote–sensing techniques ranging from weather radar to various satellite–based sensors. Conversion of the remote–sensed signal into rainfall rates, and hence into runoff for a given river basin, is a complex and difficult process using traditional approaches. Neural–network models hold the possibility of circumventing these difficulties by training the network to map rainfall patterns into various measures of runoff that may be of interest. To investigate the potential of this approach, a very simple 5 × 5 grid cell synthetic watershed is used to generate runoff from stochastically generated rainfall patterns. A back–propagation neural network is trained to predict the peak discharge and the time of peak resulting from a single rainfall pattern. Additionally, the neural network is trained to map a time series of three rainfall patterns into a continuum of discharges over future time by using a discrete Fourier series fit to the runoff hydrograph.

316 citations