scispace - formally typeset
Search or ask a question
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.
Citations
More filters
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]....

    [...]

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
More filters
Journal ArticleDOI
TL;DR: In this paper, a dynamic programming (DP) model was used to improve the operation and efficient management of available water for the Aliyar Dam in Tamil Nadu, India, using a neural network procedure (DPN) and using a multiple linear regression procedure (DPR) model.
Abstract: Reservoir operating policies are derived to improve the operation and efficient management of available water for the Aliyar Dam in Tamil Nadu, India, using a dynamic programming (DP) model, a stochastic dynamic programming (SDP) model, and a standard operating policy (SOP). The objective function for this case study is to minimize the squared deficit of the release from the irrigation demand. From the DP algorithm, general operating policies are derived using a neural network procedure (DPN model), and using a multiple linear regression procedure (DPR model). The DP functional equation is solved for 20 years of fortnightly historic data. The field irrigation demand is computed for this study by the modified Penman method with daily meteorological data. The performance of the DPR, DPN, SDP, and SOP models are compared for three years of historic data, using the proposed objective function. The neural network procedure based on the dynamic programming algorithm provided better performance than the other mo...

171 citations

Journal ArticleDOI
TL;DR: The results of using the algorithm for the 48 cases demonstrate the significant value of the algorithm in selecting reservoir operating rules.
Abstract: The development of general reservoir system operating rules by deterministic optimization is investigated in this paper. An algorithm that cycles through a deterministic dynamic program, a regression analysis, and a simulation model is proposed and tested for 48 cases: annual operating rules are determined for 12 cases, and monthly operating rules are determined for 36 cases. The algorithm is easy to use, and each component of the algorithm is relatively simple. The results of using the algorithm for the 48 cases demonstrate the significant value of the algorithm in selecting reservoir operating rules.

132 citations

Journal ArticleDOI
TL;DR: In this article, a single multiple-purpose reservoir is analyzed using a backward looking dynamic program algorithm to obtain optimal releases, and the dynamic program is solved for both one-sided and two-sided quadratic loss functions.
Abstract: A single multiple-purpose reservoir is analyzed using a backward looking dynamic program algorithm to obtain optimal releases. The dynamic program is solved for both one-sided and two-sided quadratic loss functions. Monthly policies are derived by regressing the optimal set of releases on the input and state variables. Linear and nonlinear release policies are developed, then verified and compared through simulation. For a two-sided quadratic loss function, linear policies are as good or better than nonlinear policies. However, for a one-sided quadratic loss function, nonlinear policies give improved performance over linear policies. It is also illustrated that the maximum R2 criterion for selecting release policies may not always be appropriate. Hoover Reservoir, located on Big Walnut Creek in central Ohio, is used as a case example.

117 citations

Journal ArticleDOI
TL;DR: In this paper, two dynamic programming models, one deterministic and one stochastic, are compared to generate reservoir operating rules. And the results show that the DPR generated rules are more effective in the operation of medium to very large reservoirs and the SDP generated rules were more effective for small reservoirs.
Abstract: Two dynamic programming models — one deterministic and one stochastic — that may be used to generate reservoir operating rules are compared. The deterministic model (DPR) consists of an algorithm that cycles through three components: a dynamic program, a regression analysis, and a simulation. In this model, the correlation between the general operating rules, defined by the regression analysis and evaluated in the simulation, and the optimal deterministic operation defined by the dynamic program is increased through an iterative process. The stochastic dynamic program (SDP) describes streamflows with a discrete lag-one Markov process. To test the usefulness of both models in generating reservoir operating rules, real-time reservoir operation simulation models are constructed for three hydrologically different sites. The rules generated by DPR and SDP are then applied in the operation simulation model and their performance is evaluated. For the test cases, the DPR generated rules are more effective in the operation of medium to very large reservoirs and the SDP generated rules are more effective for the operation of small reservoirs.

117 citations

Journal ArticleDOI
TL;DR: An implicitly stochastic optimization scheme previously described in the literature is extended to consider multiple reservoir systems and attempts to improve the initial operating rules for the system.
Abstract: An implicitly stochastic optimization scheme previously described in the literature is extended to consider multiple reservoir systems. The scheme comprises a three‐step cyclic procedure that attempts to improve the initial operating rules for the system. The system requires two sets of contemporaneous streamflow series to be used in the simulation model and synthetically generated series are required for this purpose. The three‐step cycle begins with an optimization of reservoir operations for a given set of streamflows. The optimal operations from the solution are then analyzed in a regression procedure to obtain a set of operating rules. These rules are evaluated in a simulation model using a different set of data. Based on the simulation results, bounds are placed on operations and cycle returns to the optimization model. The cycle continues until one of the stopping rules is satisfied. The use of the scheme to generate operating rules for multiple reservoir systems is illustrated for a two‐river syst...

93 citations