Rainfall-Runoff Simulation in Ungauged Tributary Streams Using Drainage Area Ratio-Based Multivariate Adaptive Regression Spline and Random Forest Hybrid Models

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dc.authorid0000-0002-5662-9479
dc.authorid0000-0002-4767-6660
dc.authorid0000-0003-0559-5261
dc.contributor.authorVaheddoost, Babak
dc.contributor.authorSafari, Mir Jafar Sadegh
dc.contributor.authorYilmaz, Mustafa Utku
dc.date.accessioned2026-02-12T21:05:36Z
dc.date.available2026-02-12T21:05:36Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractFor various reasons, it is not always possible to obtain adequate and reliable long-term streamflow records in a river basin. It is known that streamflow records are even shorter when the stations located on tributary channels are of the interest. Hence, it is necessary to develop dependable streamflow estimation models for the tributary streams that play a key role in the micro-hydrology of the basin. In this study, rainfall-runoff models are developed to estimate the daily streamflow in ungauged tributary streams. Precipitation and streamflow in the most similar gauging station on the main channel and lagged values up to three days before on the same tributary station are used as the input variables of the allocated models. To select the most similar gauging station, a similarity index criterion is developed and used in the analysis. Then, two scenarios based on the streamflow or the corresponding set of direct runoff and base-flow in the same station are used. By applying multivariate adaptive regression spline (MARS) and random forest (RF) methods, several rainfall-runoff models are developed and evaluated based on determination coefficient, mean absolute percentage error, root mean square error, relative peak flow, scatter plot and time series plot. Alternatively, the MARS and RF models are combined with a drainage area ratio (DAR) model to produce the DAR-MARS and DAR-RF models. It is concluded that the direct runoff in the mainstream is more effective on the streamflow of the tributary station, while the integration of models with DAR enhanced the capabilities of the models in estimation of extreme values in the streamflow time series.
dc.identifier.doi10.1007/s00024-022-03209-3
dc.identifier.endpage382
dc.identifier.issn0033-4553
dc.identifier.issn1420-9136
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85145553468
dc.identifier.scopusqualityQ2
dc.identifier.startpage365
dc.identifier.urihttps://doi.org/10.1007/s00024-022-03209-3
dc.identifier.urihttps://hdl.handle.net/20.500.12885/7048
dc.identifier.volume180
dc.identifier.wosWOS:000907825100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Basel Ag
dc.relation.ispartofPure and Applied Geophysics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260212
dc.subjectBase-flow separation
dc.subjectCoruh River
dc.subjectdrainage area ratio
dc.subjectsimilarity index
dc.subjectungauged basin
dc.subjectTurkey
dc.titleRainfall-Runoff Simulation in Ungauged Tributary Streams Using Drainage Area Ratio-Based Multivariate Adaptive Regression Spline and Random Forest Hybrid Models
dc.typeArticle

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