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

Model 1943 surface