A neural networks approach for deriving irrigation reservoir operating rules

A neural networks approach is applied to the derivation of the operating rules of an irrigation supply reservoir. Operating rules are determined as a two step process: first, a dynamic programming technique, which determines the optimal releases by minimizing the sum of squared deficits, assumed as objective function, subject to various constraints is applied. Then, the resulting releases from the reservoir are expressed as a function of significant variables by neural networks. Neural networks are trained on a long period, including severe drought events, and the operation rules so determined are validated on a different shorter period. The behaviour of different operating rules is assessed by simulating reservoir operation and by computing several performance indices of the reservoir and crop yield through a soil water balance model. Results show that operating rules based on an optimization with constraints resembling real system operation criteria lead to a good performance both in normal and in drought periods, reducing maximum deficits and water spills.