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Enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting

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dc.citedby2
dc.contributor.authorAbozweita O.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorMohd Sidek L.B.en_US
dc.contributor.authorBasri H.B.en_US
dc.contributor.authorBin Zawawi M.H.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57219806365en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid58617132200en_US
dc.contributor.authorid57065823300en_US
dc.contributor.authorid59456402500en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2025-03-03T07:41:33Z
dc.date.available2025-03-03T07:41:33Z
dc.date.issued2024
dc.description.abstractThe utilisation of modelling tools in hydrology has been effective in predicting future floods by analysing historical rainfall and inflow data, due to the association between climate change and flood frequency. This study utilised a historical dataset of monthly inflow and rainfall for the Terengganu River in Malaysia, and it is renowned for its hydrological patterns that exhibit a high level of unpredictability. The evaluation of the predictive precision and effectiveness of the Optimised Decision Tree ODT model, along with the RF and GBT models, in this study involved analysing several indicators. These indicators included the correlation coefficient, mean absolute error, percentage of relative error, root mean square error, Nash-Sutcliffe efficiency, and accuracy rate. The research results indicated that the ODT and RF models performed better than the GBT model in predicting monthly inflows. The ODT model, as well as the RF and GBT models, showed validation results with average accuracies of 94%, 91%, and 92%, respectively. The R2 values were 90.2%, 84.8%, and 96.0%, respectively, and the NES values ranged from 0.92 to 0.94. The results of this research have greater implications, extending beyond the forecasting of monthly inflow rates to encompass other hydro-meteorological variables that depend exclusively on historical input data. ? 2024 The Authors.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.2166/hydro.2024.205
dc.identifier.epage2703
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85210912702
dc.identifier.spage2683
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85210912702&doi=10.2166%2fhydro.2024.205&partnerID=40&md5=c07845428854934f262655e68bcad87b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36200
dc.identifier.volume26
dc.pagecount20
dc.publisherIWA Publishingen_US
dc.sourceScopus
dc.sourcetitleJournal of Hydroinformatics
dc.titleEnhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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