Publication:
Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations

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dc.citedby4
dc.contributor.authorKumar M.en_US
dc.contributor.authorElbeltagi A.en_US
dc.contributor.authorPande C.B.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorChow M.F.en_US
dc.contributor.authorPham Q.B.en_US
dc.contributor.authorKumari A.en_US
dc.contributor.authorKumar D.en_US
dc.contributor.authorid57806584200en_US
dc.contributor.authorid57204724397en_US
dc.contributor.authorid57193547008en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid57208495034en_US
dc.contributor.authorid57219317550en_US
dc.contributor.authorid57212688058en_US
dc.date.accessioned2023-05-29T09:37:38Z
dc.date.available2023-05-29T09:37:38Z
dc.date.issued2022
dc.descriptionDecision trees; Errors; Flood control; Floods; Mean square error; Statistical tests; Agriculture management; Burhabalang river; Daily discharge; Data-driven model; Discharge estimation; Flood management; Training and testing; Water flood; Water industries; Water resources management; Rivers; algorithm; error analysis; estimation method; flood control; modeling; river discharge; river flow; Indiaen_US
dc.description.abstractAccurate and reliable discharge estimation is considered vital in managing water resources, agriculture, industry, and flood management on the basin scale. In this study, five data-driven tree-based algorithms: M5-Pruned model-M5P (Model-1), Random Forest-RF (Model-2), Random Tree-RT (Model-3), Reduced Error Pruning Tree-REP Tree (Model-4), and Decision Stump-DS (Model-5) have been examined to measure the daily discharge of Govindpur site at Burhabalang river, India. The proposed models will be calibrated by daily 10-years time-series hydrological data (i.e., river stage (h) and daily discharge (Q)) measured from 2004 to 2013. In these models, 70% and 30% of the dataset were used for the training and testing stage for the reliability of the developed models. The precision of the models was optimized by investigating five different scenarios based on various time-lags combinations. Model�s performance has been assessed and evaluated using five statistical metrics, namely, correlation coefficient (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE). Results showed that Model-3 outperforms as compared to other proposed models. Machine learning models have been examined five scenarios of input variables during training and testing phases. In comparison of the Model-5 struggled in capturing the river's flow rate and showed poor performance in scenarios where R2 metric values ranged from 0.64 to 0.94. Therefore, it can be concluded that the RT model could be used as a robust model for sustainable flood plain management. � 2022, The Author(s), under exclusive licence to Springer Nature B.V.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11269-022-03136-x
dc.identifier.epage2221
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85128754241
dc.identifier.spage2201
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85128754241&doi=10.1007%2fs11269-022-03136-x&partnerID=40&md5=7aaee30b8ac7be3921a9487f7722a3f2
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26895
dc.identifier.volume36
dc.publisherSpringer Science and Business Media B.V.en_US
dc.sourceScopus
dc.sourcetitleWater Resources Management
dc.titleApplications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinationsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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