Predictive models of reservoir storage-yield-reliability functions: Inter-comparison of regression and multi-layer perceptron artificial neural network paradigms

Adebayo Adeloye

Predictive models of reservoir storage-yield-reliability functions: Inter-comparison of regression and multi-layer perceptron artificial neural network paradigms

Adebayo Adeloye

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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/Territory	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

Cite this

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title = "Predictive models of reservoir storage-yield-reliability functions: Inter-comparison of regression and multi-layer perceptron artificial neural network paradigms",

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 {\textcopyright} 2007 IAHS Press.",

keywords = "Artificial neural networks, Multiple regression, Over-year capacity, Sequent peak algorithm, Storage-yield-reliability, Within, Year capacity",

author = "Adebayo Adeloye",

year = "2007",

language = "English",

isbn = "9781901502091",

pages = "479--486",

booktitle = "IAHS-AISH Publication - Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management",

edition = "313",

note = "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) ; Conference date: 02-07-2007 Through 13-07-2007",

}

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 edn, pp. 479-486, 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, 2/07/07.

Predictive models of reservoir storage-yield-reliability functions : Inter-comparison of regression and multi-layer perceptron artificial neural network paradigms. / Adeloye, Adebayo.

IAHS-AISH Publication - Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management. 313. ed. 2007. p. 479-486.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Predictive models of reservoir storage-yield-reliability functions

T2 - 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)

AU - Adeloye, Adebayo

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AB - 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.

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KW - Within

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BT - IAHS-AISH Publication - Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management

Y2 - 2 July 2007 through 13 July 2007

ER -

Predictive models of reservoir storage-yield-reliability functions: Inter-comparison of regression and multi-layer perceptron artificial neural network paradigms

Abstract

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Keywords

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