Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/7117
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dc.contributor.authorTayfur, Gökmen-
dc.contributor.authorSingh, Vijay P.-
dc.contributor.authorMoramarco, Tommaso-
dc.contributor.authorBarbetta, Silvia-
dc.date.accessioned2019-02-19T12:23:55Z-
dc.date.available2019-02-19T12:23:55Z-
dc.date.issued2018-07-
dc.identifier.citationTayfur, G., Singh, V. P., Moramarco, T., and Barbetta, S. (2018). Flood hydrograph prediction using machine learning methods. Water, 10(8). doi:10.3390/w10080968en_US
dc.identifier.issn2073-4441-
dc.identifier.urihttp://doi.org/10.3390/w10080968-
dc.identifier.urihttp://hdl.handle.net/11147/7117-
dc.description.abstractMachine learning (soft) methods have a wide range of applications in many disciplines, including hydrology. The first application of these methods in hydrology started in the 1990s and have since been extensively employed. Flood hydrograph prediction is important in hydrology and is generally done using linear or nonlinear Muskingum (NLM) methods or the numerical solutions of St. Venant (SV) flow equations or their simplified forms. However, soft computing methods are also utilized. This study discusses the application of the artificial neural network (ANN), the genetic algorithm (GA), the ant colony optimization (ACO), and the particle swarm optimization (PSO) methods for flood hydrograph predictions. Flow field data recorded on an equipped reach of Tiber River, central Italy, are used for training the ANN and to find the optimal values of the parameters of the rating curve method (RCM) by the GA, ACO, and PSO methods. Real hydrographs are satisfactorily predicted by the methods with an error in peak discharge and time to peak not exceeding, on average, 4% and 1%, respectively. In addition, the parameters of the Nonlinear Muskingum Model (NMM) are optimized by the same methods for flood routing in an artificial channel. Flood hydrographs generated by the NMM are compared against those obtained by the numerical solutions of the St. Venant equations. Results reveal that the machine learning models (ANN, GA, ACO, and PSO) are powerful tools and can be gainfully employed for flood hydrograph prediction. They use less and easily measurable data and have no significant parameter estimation problem.en_US
dc.language.isoenen_US
dc.publisherMDPI Multidisciplinary Digital Publishing Instituteen_US
dc.relation.ispartofWater (Switzerland)en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHydrograph predictionsen_US
dc.subjectMachine learning methodsen_US
dc.subjectNonlinear Muskingum modelen_US
dc.subjectRating curve methoden_US
dc.subjectSt. Venant equationsen_US
dc.titleFlood hydrograph prediction using machine learning methodsen_US
dc.typeArticleen_US
dc.authoridTR2054en_US
dc.contributor.departmentIzmir Institute of Technology. Civil Engineeringen_US
dc.identifier.volume10en_US
dc.identifier.issue8en_US
dc.identifier.wosWOS:000448462700002
dc.identifier.scopusSCOPUS:2-s2.0-85050485328
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.3390/w10080968-
dc.relation.doi10.3390/w10080968en_US
dc.coverage.doi10.3390/w10080968en_US
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairetypeArticle-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.deptDepartment of Civil Engineering-
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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