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Dam management with imperfect models: bayesian model averaging and neural network control

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Figure

Fig. 1. The first 20 years of the historical data generated by Equation 1, overlaid withthe single best-fit model with parameters w0 = 0.32 and w1 = 6.9964 in Equation 2.
Fig. 2. The probability distribution of plausible models. The single most-probablemodel is the one at the peak of the highest hill.
Fig. 3. There are an unlimited number of low-probability models, so this paper uses acutoff of 10−6, which makes for 1,489 models.
Fig. 4. A simple control policy is to gradually release water to bring the level down tosome fixed percentage
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