Neelakantan, T.R., Pundarikanthan, N.V. (2000) "Neural Network Based Simulation-Optimization Model for Reservoir Operation" J Water Res. Plan. Mgmt. 126(2):57-64
The authors of this paper used a combined neural network simulation optimization approach to study the optimal management policy for a series of water supply reservoirs in Chennai, India. The neural network based approach provided a large advantage over traditional simulation approaches in terms of analytical speed. The increased computation speed came at the expense of supporting data in that the neural network was designed to provide output in terms of the final value of the optimization function and did not provide any information about the status of other variable within the system. To overcome some of this the authors used the optimal and near optimal results from the neural network as fodder for more traditional Hooke and Jeeves optimization that also allowed them to gather information regarding delivery shortfalls, spillage events and evaporative losses in the system.
To be honest I didn't completely follow the deficiency index system that the authors put forward as an evaluative criteria the inclusion of an equity measure in the index didn't make sense to me on first or second readings. I'm not clear on why equity of shortfalls was included in the evaluation, is this an institutional constraint? Otherwise the paper was fairly impressive, the relative speed with which the neural network was able to handle the neccissary simulations for each scenario (36,000-54,000 runs) is worth noting. The idea of using multiple methods for analysis of water resource problems also seems to be a re-occuring theme in the papers so far. By using the neural network to do the "heavy lifting" the authors were able to find a collection of solutions that could then be further analysed by more traditional methods.