de Rigo, Daniele and Rizzoli, Andrea Emilio and Soncini-Sessa, Rodolfo and Weber, Enrico and Zenesi, Pietro
(2001):
*Neuro-dynamic programming for the efficient management of reservoir networks.*
Published in: Proceedings of MODSIM 2001, International Congress on Modelling and Simulation
, Vol. 4,
(December 2001): pp. 1949-1954.

Preview |
PDF
MPRA_paper_42233.pdf Download (186kB) | Preview |

## Abstract

The management of a water reservoir can be improved thanks to the use of stochastic dynamic programming (SDP) to generate management policies which are efficient with respect to the management objectives (flood protection, water supply for irrigation and hydropower generation, respect of minimum environmental flows, etc.). The improvement in efficiency is even more remarkable when the problem involves a reservoir network, that is a set of reservoirs which are interconnected. Unfortunately, SDP is affected by the “curse of dimensionality” and computing time and computer memory occupation can quickly become unbearable. Neuro-dynamic programming (NDP) can sensibly reduce the demands on computer time and memory thanks to the approximation of Bellman functions with Artificial Neural Networks (ANNs). In this paper an application of neuro-dynamic programming to the problem of the management of reservoir networks is presented.

Item Type: | MPRA Paper |
---|---|

Original Title: | Neuro-dynamic programming for the efficient management of reservoir networks |

Language: | English |

Keywords: | Water reservoir management; Stochastic dynamic programming; Neuro-dynamic programming |

Subjects: | C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O13 - Agriculture ; Natural Resources ; Energy ; Environment ; Other Primary Products P - Economic Systems > P2 - Socialist Systems and Transitional Economies > P28 - Natural Resources ; Energy ; Environment Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q2 - Renewable Resources and Conservation > Q25 - Water Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q0 - General N - Economic History > N5 - Agriculture, Natural Resources, Environment, and Extractive Industries C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis |

Item ID: | 42233 |

Depositing User: | Daniele de Rigo |

Date Deposited: | 06 Nov 2012 16:47 |

Last Modified: | 01 Oct 2019 05:25 |

References: | [1] Bellman, R.E., and Dreyfus, S.E., Functional approximations and dynamic programming, Mathematical Tables and Other Aids to Computation, 13, pp. 247–251, 1959. [2] Bertsekas, D.P., and J.N. Tsitsiklis, Neuro-Dynamic Programming, Athena Scientific, Belmont, MA, 1996. [3] Georgakakos, A.P., and Marks, D.H., A new method for real-time operation of reservoir systems, Water Resour. Res., 23(7), pp. 1376–1390. 1987. [4] Georgakakos, A.P., Extended Linear Quadratic Gaussian Control for the real-time operation of reservoir systems, in Dynamic Programming for Optimal Water Resources Systems Analysis, A. Esogbue, ed., Prentice Hall Publishing Company, NJ, pp. 329–360, 1989. [5] Hagan, M.T., and M. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks, 5(6), pp. 989–993, 1994. [6] Hornik, K., Multilayer feedforward networks are universal approximators, Neural Networks, 2, pp. 359–366, 1989. [7] Kreinovich, V., Arbitrary nonlinearity is sufficient to represent all functions by neural networks: a theorem, Neural Networks, vol.4, pp. 381–383, 1991. [8] Nardini, A, C. Piccardi and R. Soncini-Sessa, A decomposition approach to suboptimal control of discrete-time systems, Optimal Control Applications and Methods, 15, pp. 1–12, 1994. [9] Piccardi, C. and R. Soncini-Sessa, Stochastic dynamic programming for reservoir optimal control: dense discretization and inflow correlation assumption made possible by parallel computing. Water Resour. Res., 27(2), pp. 729–741, 1991. [10] Rumelhart, D.E., G.E. Hinton, and R.J. Williams, Learning internal representations by error backpropagation, in Parallel Data Processing, D.E. Rumelhart and J.L. McClelland, eds., vol 1, Cambridge, MA: The MIT Press, pp. 318–362, 1986. |

URI: | https://mpra.ub.uni-muenchen.de/id/eprint/42233 |