Abstract
In this study, functions for predicting the total (within-year plus over-year) reservoir capacity have been developed using: first classical multiple regression, and secondly artificial neural networks (ANNs). The basis of the models is the storage-yield-reliability (S-Y-R) analysis of 18 international rivers using the sequent-peak algorithm(SPA). The results showed that the regression model performed better than the ANN model. The relative superiority of the regression model was attributed to its use of the over-year capacity as an independent variable. In contrast, the ANNs use basic variables as inputs and thus offer more flexibility than the regression model, particularly at ungauged sites. Copyright © 2007 IAHS Press.
| Original language | English |
|---|---|
| Title of host publication | IAHS-AISH Publication - Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management |
| Pages | 479-486 |
| Number of pages | 8 |
| Edition | 313 |
| Publication status | Published - 2007 |
| Event | International Symposium: Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management - 24th General Assembly of the International Union of Geodesy and Geophysics (IUGG) - Perugia, Italy Duration: 2 Jul 2007 → 13 Jul 2007 |
Conference
| Conference | International Symposium: Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management - 24th General Assembly of the International Union of Geodesy and Geophysics (IUGG) |
|---|---|
| Country | Italy |
| City | Perugia |
| Period | 2/07/07 → 13/07/07 |
Keywords
- Artificial neural networks
- Multiple regression
- Over-year capacity
- Sequent peak algorithm
- Storage-yield-reliability
- Within
- Year capacity
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Adeloye, A. (2007). Predictive models of reservoir storage-yield-reliability functions: Inter-comparison of regression and multi-layer perceptron artificial neural network paradigms. In IAHS-AISH Publication - Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management (313 ed., pp. 479-486)