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A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia

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dc.citedby2
dc.contributor.authorHanoon M.S.en_US
dc.contributor.authorAbdullatif B A.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorRazzaq A.en_US
dc.contributor.authorBirima A.H.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57266877500en_US
dc.contributor.authorid57290875300en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57219410567en_US
dc.contributor.authorid23466519000en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:38:09Z
dc.date.available2023-05-29T09:38:09Z
dc.date.issued2022
dc.descriptioncomparative study; machine learning; river system; sensitivity analysis; suspended load; suspended sediment; water resource; Johor; Johor River; Krakatau; Lampung; Malaysia; Panjang; West Malaysiaen_US
dc.description.abstractAccurate and reliable suspended sediment load (SSL) prediction models are necessary for the planning and management of water resource structures. In this study, four machine learning techniques, namely Gradient boost regression (GBT), Random Forest (RF), Support vector machine (SVM), and Artificial neural network ANN will be developed to predict SSL at the Rantau Panjang station on Johor River basin (JRB), Malaysia. Four evaluation criteria, including the Correlation Coefficient (R), Root Mean Square Error (RMSE), Nash Sutcliffe Efficiency (NSE) and Scatter Index (SI) will utilize to evaluating the performance of the proposed models. The obtained results revealed that all the proposed Machine Learning (ML) models showed superior prediction daily SSL performance. The comparative outcomes among models were carried out using the Taylor diagram. ANN model shows more reliable results than other models with R of 0.989, SI of 0.199, RMSE of 0.011053 and NSE of 0.979. A sensitivity analysis of the models to the input variables revealed that the absence of current day Suspended sediment load data SSLt-1 had the most effect on the SSL. Moreover, to examine validation of most accurate model we proposed divided data to 50% training, 25% testing and 25% validation) sets and ANN provided superior performance. Therefore, the proposed ANN approach is recommended as the most accurate model for SSL prediction. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s12145-021-00689-0
dc.identifier.epage104
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85116731881
dc.identifier.spage91
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85116731881&doi=10.1007%2fs12145-021-00689-0&partnerID=40&md5=0f6866dafa5bc236caa32b3df5700146
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26960
dc.identifier.volume15
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleEarth Science Informatics
dc.titleA comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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