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DC Field | Value | Language |
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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 |
Files in This Item:
File | Description | Size | Format | |
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Bachir Sakaa.pdf | 493,2 kB | Adobe PDF | View/Open |
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