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Journal ArticleDOI

Multireservoir Modeling with Dynamic Programming and Neural Networks

01 Apr 2001-Journal of Water Resources Planning and Management (American Society of Civil Engineers)-Vol. 127, Iss: 2, pp 89-98
TL;DR: The multireservoir model based on the dynamic programming-neural network algorithm gives improved performance in this study.

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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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Citations
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Journal ArticleDOI
John W. Labadie1Institutions (1)
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.

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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.

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1,370 citations


Journal ArticleDOI
Deepti Rani1, Maria Madalena Moreira1Institutions (1)
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.

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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.

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391 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.

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Abstract: In this study, the GLUE methodology is applied to establish the sensitivity of flood inundation predictions to uncertainty of the upstream boundary condition and bridges within the modelled region. An understanding of such uncertainties is essential to improve flood forecasting and floodplain mapping. The model has been evaluated on a large data set. This paper shows 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. The type of bridge implementation can have local effects, which is strongly influenced by the bridge geometry (in this case the area of the culvert). However, the type of bridge will not merely influence the model performance within the region of the structure, but also other evaluation criteria such as the travel time. This also highlights the difficulties in establishing which parameters have to be more closely examined in order to achieve better fits. In this study no parameter set or model implementation that fulfils all evaluation criteria could be established. We propose four different approaches to this problem: closer investigation of anomalies; introduction of local parameters; increasing the size of acceptable error bounds; and resorting to local model evaluation. Moreover, we show that it can be advantageous to decouple the classification into behavioural and non-behavioural model data/parameter sets from the calculation of uncertainty bounds.

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314 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
Ajay Singh1Institutions (1)
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.

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Abstract: Summary The optimal use of available resources is of paramount importance in the backdrop of the increasing food, fiber, and other demands of the burgeoning global population and the shrinking resources. The optimal use of these resources can be determined by employing an optimization technique. The comprehensive reviews on the use of various programming techniques for the solution of different optimization problems have been provided in this paper. The past reviews are grouped into nine sections based on the solutions of the theme-based real world problems. The sections include: use of optimization modelling for conjunctive use planning, groundwater management, seawater intrusion management, irrigation management, achieving optimal cropping pattern, management of reservoir systems operation, management of resources in arid and semi-arid regions, solid waste management, and miscellaneous uses which comprise, managing problems of hydropower generation and sugar industry. Conclusions are drawn where gaps exist and more research needs to be focused.

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168 citations


Journal ArticleDOI
Alcigeimes B. Celeste1, Max Billib1Institutions (1)
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.

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Abstract: This paper investigates the performance of seven stochastic models used to define optimal reservoir operating policies. The models are based on implicit (ISO) and explicit stochastic optimization (ESO) as well as on the parameterization–simulation–optimization (PSO) approach. The ISO models include multiple regression, two-dimensional surface modeling and a neuro-fuzzy strategy. The ESO model is the well-known and widely used stochastic dynamic programming (SDP) technique. The PSO models comprise a variant of the standard operating policy (SOP), reservoir zoning, and a two-dimensional hedging rule. The models are applied to the operation of a single reservoir damming an intermittent river in northeastern Brazil. The standard operating policy is also included in the comparison and operational results provided by deterministic optimization based on perfect forecasts are used as a benchmark. In general, the ISO and PSO models performed better than SDP and the SOP. In addition, 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.

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139 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.

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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.

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1,287 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.

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Abstract: A neural network is developed to forecast rainfall intensity fields in space and time; it is a three-layer learning network with input, hidden, and output layers. Training is conducted using back propagation where the input and output rainfall fields are presented to the neural network as a series of learning sets. After training is complete, the neural network is used to forecast rainfall intensity fields with a lead time of 1 h using only the current field as input. Rainfall fields are generated using a space-time mathematical rainfall simulation model, and forecasted fields are compared with the perfectly known model-produced fields. Results indicate that a neural network is capable of learning the complex relationship describing the space-time evolution of rainfall such as that inherent in a complex rainfall simulation model. One hour ahead forecasts are produced, and comparisons with true mean areal intensities and percent areal coverage indicate that in most cases the method performs well when applied to the events used in training. The neural network is used to forecast a series of events not included in the training data and is shown to perform well when a relatively large number of hidden nodes are utilized. Performance of the neural network is compared with two other methods of short-term forecasting, persistence and nowcasting.

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636 citations


Journal ArticleDOI
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.

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498 citations


Journal ArticleDOI
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 ...

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475 citations


Journal ArticleDOI
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.

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308 citations


Performance
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No. of citations received by the Paper in previous years
YearCitations
20212
202011
20192
20185
201710
20168