Waffen SS grenadier with steel helmet and assault rifle 44 in the crook of his arm. 94% with several small chips along the mid and lower left edge. The right side of the helmet features an SS Sig-Rune decal which is approx. The helmet shell is marked with a very faint maker's code, and the size "66." It features a semi-rough field gray finish, which is approx. Both models offer avenues for predicting reservoir capacity at gauged sites without the expense of time-series based simulation alternatives.
The results showed that all the models performed well during development and when tested with independent data sets. The inputs used for the ANNs were basic runoff and systems variables such as the coefficient of variation of annual and monthly runoff, minimum monthly runoff, the demand ratio, and reliability. However, the ANNs were used to simultaneously model directly the intrinsically nonlinear over-year and total (i.e., within-year+over-year) capacity-yield-reliability functions. Linear regression was used to model the total capacity using the over-year capacity as one of the inputs. © 2009 ASCE.ĪB - Generalized models for predicting the storage-yield-reliability functions of surface water reservoirs are developed using multiple linear regression and multilayer perceptron, artificial neural networks (ANNs).
N2 - Generalized models for predicting the storage-yield-reliability functions of surface water reservoirs are developed using multiple linear regression and multilayer perceptron, artificial neural networks (ANNs). T1 - Multiple linear regression and artificial neural networks models for generalized reservoir storage-yield-reliability function for reservoir planning
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