A mathematical framework that incorporates risk attitudes into a management model for a reservoir supplying water to an irrigation district is proposed. The attitudes of the agricultural procedures are captured within a neural network which in turn is embedded into a stochastic dynamic programming (SDP) formulation. Rather than using the expectation of the return function to drive the SDP model, the neural network identifies the preferred alternative according to its learned risk attitudes. The advantage of this approach is that the risk attitudes can be utilized by the model without the use of surrogate measures.
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