Decision tree (DT), generalized regression neural network (GR) and multivariate adaptive regression splines (MARS) models for sediment transport in sewer pipes

dc.contributor.author	Safari, M.J.S.
dc.date.accessioned	2021-01-25T20:48:16Z
dc.date.available	2021-01-25T20:48:16Z
dc.date.issued	2019
dc.identifier	10.2166/wst.2019.106
dc.identifier.issn	02731223
dc.identifier.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065777183&doi=10.2166%2fwst.2019.106&partnerID=40&md5=6f82d0f6752f99726d04c97e37ad48b7
dc.identifier.uri	https://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/9844
dc.description.abstract	Sediment deposition in sewers and urban drainage systems has great effect on the hydraulic capacity of the channel. In this respect, the self-cleansing concept has been widely used for sewers and urban drainage systems design. This study investigates the
dc.language.iso	English
dc.publisher	Water Science and Technology
dc.title	Decision tree (DT), generalized regression neural network (GR) and multivariate adaptive regression splines (MARS) models for sediment transport in sewer pipes
dc.type	Article
dc.relation.firstpage	1113
dc.relation.lastpage	1122
dc.relation.volume	79
dc.relation.issue	6
dc.description.affiliations	Department of Civil Engineering, Yaşar University, Izmir, Turkey

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