A Monthly Rainfall–Runoff Modelling using Multiple Linear Regression and Artificial Neural Network Techniques

Please use this identifier to cite or link to this item: https://dspace.univ-ouargla.dz/jspui/handle/123456789/25331

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DC Field	Value	Language
dc.contributor.author	Sakaa, Bachir	-
dc.contributor.author	Brahima, Nabil	-
dc.contributor.author	Chaffai, Hicham	-
dc.contributor.author	Hani, Azzeddine	-
dc.date.accessioned	2021-04-05T00:39:14Z	-
dc.date.available	2021-04-05T00:39:14Z	-
dc.date.issued	2019-10-16	-
dc.identifier.uri	http://dspace.univ-ouargla.dz/jspui/handle/123456789/25331	-
dc.description	Séminaire International sur l′Hydrogéologie et l′Environnement SIHE 2019 Ouargla	en_US
dc.description.abstract	The study presents multiple linear regression MLR coupled with multilayer perceptron MLP for predicting monthly runoff (Qt). The data set including total 348 data records is divided into two subsets, training and testing. Various models depending on the combination of antecedent values of monthly runoff and rainfall are constructed and the best fit input structure is examined. The performance of models in training and testing phases are compared with the observed monthly runoff values...	en_US
dc.publisher	Université Kasdi Merbah Ouargla	en_US
dc.subject	MLR, BFGS algorithm, SCG algorithm, monthly runoff, modeling	en_US
dc.title	A Monthly Rainfall–Runoff Modelling using Multiple Linear Regression and Artificial Neural Network Techniques	en_US
dc.type	Article	en_US
Appears in Collections:	4. Faculté des Hydrocarbures, des Energies Renouvelables, des Sciences de la Terre et de l’Univers

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