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DC Field | Value | Language |
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dc.contributor.author | BOULMAIZ, Tayeb | - |
dc.date.accessioned | 2016-10-16T15:29:25Z | - |
dc.date.available | 2016-10-16T15:29:25Z | - |
dc.date.issued | 2016 | - |
dc.identifier.uri | http://dspace.univ-ouargla.dz/jspui/handle/123456789/11329 | - |
dc.description.abstract | Contribution dans la modélisation de la relation pluie-débits Le but de cet étude est de modéliser le phénomène de la relation pluie-débit, pour y arriver, trois approches différentes sont testées : modélisation conceptuelle, modélisation avec deux des méthodes issus de l’intelligence artificiel (un réseau de neurones artificiel et un système d’inférence à base de réseau adaptif) et enfin une combinaison des modèles conceptuel et neuronal. Les résultats obtenus ont démontré que cette dernière approche est la plus robuste par rapport aux autres approches testées et c’est celle qui est préconisé, que ce soit en termes de prévision des crues ou de gestion de la ressource en eau. | en_US |
dc.description.abstract | .الهدف من هذه الدراسة هو نمذجة هذه ظاهرة علاقة تساقط – تدفق, للوصول الى ذالك, تم اختبار ثلاثة نهج مختلفة: النمذجة التصورية، والنمذجة باستعمال نموذجين من الذكاء الاصطناعي (الشبكة العصبية الاصطناعية ومنطق الاستدلال الضبابي القائم على شبكة التكيف) وأخيرا مزيج من النموذجين التصويري و الشبكي العصبي. النتائج المتحصل عليها بينت ان هذا النهج الاخير يعتبر الاكثر قوة من بين النماذج الاخرى و هو النهج الذي ينصح استعماله سواء كانت في الوقاية من الفيضانات او تسيير الموارد المائية. | - |
dc.description.abstract | Contribution in modeling rainfall-runoff relationship The aim of this study is to model the rainfall runoff relationship phenomenon, in reach this objective, three different approaches were tested: conceptual modeling, modeling withtwo artificial intelligence model(artificial neural network and an adaptive-network-based fuzzy inference system) and finally, a combination of the conceptual and neuronal models. Obtained results showed that this last approach is the most robust compared with other approaches and is the one which recommend, whether in terms of flood forecasting or management of water resource | - |
dc.description.abstract | The aim of this study is to model the rainfall runoff relationship phenomenon, in reach this objective, three different approaches were tested: conceptual modeling, modeling with two artificial intelligence model (artificial neural network and an adaptive-network-based fuzzy inference system) and finally, a combination of the conceptual and neuronal models. Obtained results showed that this last approach is the most robust compared with other approaches and is the one which recommend, whether in terms of flood forecasting or management of water resource. | - |
dc.language.iso | fr | en_US |
dc.subject | Pluie-débit | en_US |
dc.subject | GR4j | en_US |
dc.subject | Réseau de Neurone Artificiel | en_US |
dc.subject | RNA | en_US |
dc.subject | Système d’inférence flou à base de réseau adaptatif | en_US |
dc.subject | ANFIS | en_US |
dc.subject | mise à jour de modèle hydrologique | en_US |
dc.subject | Oued Rassoul | en_US |
dc.subject | تساقط | en_US |
dc.subject | تدفق | en_US |
dc.subject | منطق الاستدلال الضبابي القائم على شبكة التكيف | en_US |
dc.subject | تحديث نموذج هيدرولوجي | en_US |
dc.subject | واد غسول | en_US |
dc.title | Contribution dans la modélisation de la relation pluie-débits | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Département de Hydraulique et Génie Civil - Doctorat |
Files in This Item:
File | Description | Size | Format | |
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THESE7.pdf | 10,83 MB | Adobe PDF | View/Open |
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