Regression models for sediment transport in tropical rivers

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dc.authoridSafari, Mir Jafar Sadegh/0000-0003-0559-5261
dc.authoridGUL, ENES/0000-0001-9364-9738
dc.authoridHARUN, MOHD AFIQ/0000-0002-9210-3466
dc.authoridGhani, Aminuddin Ab/0000-0002-8912-9569
dc.authorwosidSafari, Mir Jafar Sadegh/A-4094-2019
dc.authorwosidGUL, ENES/AAH-6191-2021
dc.authorwosidHARUN, MOHD AFIQ/AAZ-7222-2021
dc.authorwosidGhani, Aminuddin Ab/B-2529-2008
dc.contributor.authorHarun, Mohd Afiq
dc.contributor.authorSafari, Mir Jafar Sadegh
dc.contributor.authorGul, Enes
dc.contributor.authorAb Ghani, Aminuddin
dc.date.accessioned2024-08-04T20:50:15Z
dc.date.available2024-08-04T20:50:15Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe investigation of sediment transport in tropical rivers is essential for planning effective integrated river basin management to predict the changes in rivers. The characteristics of rivers and sediment in the tropical region are different compared to those of the rivers in Europe and the USA, where the median sediment size tends to be much more refined. The origins of the rivers are mainly tropical forests. Due to the complexity of determining sediment transport, many sediment transport equations were recommended in the literature. However, the accuracy of the prediction results remains low, particularly for the tropical rivers. The majority of the existing equations were developed using multiple non-linear regression (MNLR). Machine learning has recently been the method of choice to increase model prediction accuracy in complex hydrological problems. Compared to the conventional MNLR method, machine learning algorithms have advanced and can produce a useful prediction model. In this research, three machine learning models, namely evolutionary polynomial regression (EPR), multi-gene genetic programming (MGGP) and M5 tree model (M5P), were implemented to model sediment transport for rivers in Malaysia. The formulated variables for the prediction model were originated from the revised equations reported in the relevant literature for Malaysian rivers. Among the three machine learning models, in terms of different statistical measurement criteria, EPR gives the best prediction model, followed by MGGP and M5P. Machine learning is excellent at improving the prediction distribution of high data values but lacks accuracy compared to observations of lower data values. These results indicate that further study needs to be done to improve the machine learning model's accuracy to predict sediment transport.en_US
dc.description.sponsorshipREDAC, USM; Public Service Department of Malaysia under the Hadiah Latihan Persekutuan (HLP) programmeen_US
dc.description.sponsorshipThe authors would like to express special thanks for the support provided by REDAC, USM. Acknowledgement also goes to the Public Service Department of Malaysia for the scholarship provided to the first author under the Hadiah Latihan Persekutuan (HLP) programme.en_US
dc.identifier.doi10.1007/s11356-021-14479-0
dc.identifier.endpage53115en_US
dc.identifier.issn0944-1344
dc.identifier.issn1614-7499
dc.identifier.issue38en_US
dc.identifier.pmid34023993en_US
dc.identifier.scopus2-s2.0-85106433451en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage53097en_US
dc.identifier.urihttps://doi.org/10.1007/s11356-021-14479-0
dc.identifier.urihttps://hdl.handle.net/11616/99952
dc.identifier.volume28en_US
dc.identifier.wosWOS:000652948100014en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofEnvironmental Science and Pollution Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine learningen_US
dc.subjectSediment transporten_US
dc.subjectTotal bed material loaden_US
dc.subjectTropical riversen_US
dc.subjectMalaysia riversen_US
dc.titleRegression models for sediment transport in tropical riversen_US
dc.typeArticleen_US

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