de Rigo, Daniele and Rizzoli, Andrea Emilio and SonciniSessa, Rodolfo and Weber, Enrico and Zenesi, Pietro (2001): Neurodynamic 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. 19491954.

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. Neurodynamic 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 neurodynamic programming to the problem of the management of reservoir networks is presented.
Item Type:  MPRA Paper 

Original Title:  Neurodynamic programming for the efficient management of reservoir networks 
Language:  English 
Keywords:  Water reservoir management; Stochastic dynamic programming; Neurodynamic 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, 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:  11. Feb 2013 22:21 
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, NeuroDynamic Programming, Athena Scientific, Belmont, MA, 1996. [3] Georgakakos, A.P., and Marks, D.H., A new method for realtime operation of reservoir systems, Water Resour. Res., 23(7), pp. 1376–1390. 1987. [4] Georgakakos, A.P., Extended Linear Quadratic Gaussian Control for the realtime 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. SonciniSessa, A decomposition approach to suboptimal control of discretetime systems, Optimal Control Applications and Methods, 15, pp. 1–12, 1994. [9] Piccardi, C. and R. SonciniSessa, 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:  http://mpra.ub.unimuenchen.de/id/eprint/42233 