Neelakantan TR, Pundarikanthan NV (2000) “Neural network-based simulation-optimization model for reservoir operation,” JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT, 126(2) pp. 57-64
The objective of this study was to optimize the operating policy of a reservoir system in Chennai, India. The study considered using a conventional simulation model and an artificial neural network, but determined that the ANN saved many days of computing time. The conventional simulation model was used to generate scenarios to train the ANN. The study utilized a Hooke and Jeeves optimization algorithm. When different inputs generated by Hooke and Jeeves were input to the ANN, the ANN gave the objective function value as the only output, which is reentered into the Hooke and Jeeves model.
The discussion of the trial scenarios and their results made very little sense to me. I thought the standard operation policy was what the reservoir managers were already doing. But the SOP generates a better objective function than the suggested operating policies. I don't see how this is an improvement or a success in the study.
I had a difficult time reading the article because I seemed to miss a lot of critical information at the beginning that had me confused the rest of the article. Really, the authors only wrote a sentence mentioning that a conventional simulation model was created in order to train the ANN, and the ANN was utilized for its quicker solve times. I missed that sentence and then went through the whole article wondering why the authors were neglecting local catchment inflows, transmission losses and percolation losses when those are irrelevant (I think) in an ANN. The full discussion of why a conventional model was necessary and why an ANN was chosen was saved until the very end and then I could go back through and understand a lot more of what was going on earlier in the article. In conclusion I'd say the only fault of the article that stuck out to me was poor order and clarity of the presentation of the material.
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Michelle,
ReplyDeleteI thought the order of presentation was strange, also. After scanning the article to get a general idea of what it was about, I ended up reading it out of order. :)
I think the neural-network solution that the authors developed improves upon SOP because the network minimizes the deficit index, spreading the drought effects out over a longer period of time. This turns a major shortage that would last for maybe only one month into a minor shortage that lasts much longer. But, one thing I still don't understand is how adding two new reservoirs to the model (Scenario 2) didn't improve the system's operation, except for slightly reducing the amount of spill.