Neuro-dynamic programming for the efficient management of reservoir networks

Daniele de Rigo, Andrea Emilio Rizzoli, Rodolfo Soncini-Sessa, Enrico Weber and Pietro Zenesi

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Water reservoir management; Stochastic dynamic programming; Neuro-dynamic programming (search for similar items in EconPapers)
JEL-codes: C45 C61 C63 N5 O13 P28 Q0 Q25 (search for similar items in EconPapers)
Date: 2001-12
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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