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Development of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, India

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dc.citedby4
dc.contributor.authorRajput J.en_US
dc.contributor.authorKushwaha N.L.en_US
dc.contributor.authorSrivastava A.en_US
dc.contributor.authorPande C.B.en_US
dc.contributor.authorSuna T.en_US
dc.contributor.authorSena D.R.en_US
dc.contributor.authorSingh D.K.en_US
dc.contributor.authorMishra A.K.en_US
dc.contributor.authorSahoo P.K.en_US
dc.contributor.authorElbeltagi A.en_US
dc.contributor.authorid57211190879en_US
dc.contributor.authorid57219726089en_US
dc.contributor.authorid57221943932en_US
dc.contributor.authorid57193547008en_US
dc.contributor.authorid57726828300en_US
dc.contributor.authorid6603383474en_US
dc.contributor.authorid57198856885en_US
dc.contributor.authorid57214672235en_US
dc.contributor.authorid57203256213en_US
dc.contributor.authorid57204724397en_US
dc.date.accessioned2025-03-03T07:42:46Z
dc.date.available2025-03-03T07:42:46Z
dc.date.issued2024
dc.description.abstractAccurate prediction of pan evaporation and mean temperature is crucial for effective water resources management, influencing the hydrological cycle and impacting water availability. This study focused on New Delhi?s semi-arid climate, data spanning 31 years (1990?2020) were used to predict these variables using advanced algorithms such as Bagging, Random Subspace (RSS), M5P, and REPTree. The models were rigorously evaluated using 10 performance metrics, including correlation coefficient, mean absolute error (MAE), and Nash?Sutcliffe Efficiency (NSE) model coefficient. The Bagging model emerged as the best model with performance indices values as r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, and MAPE as 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90, and 22.0, respectively, during model testing phase for pan evaporation prediction. In predicting mean temperature, the Bagging model reported the best results with performance indices values as r, MAE, RMSE, RAE, RRSE, MBE NSE, d, KGE, and MAPE as 0.86, 0.76, 1.43, 32.70, 49.44, 0.03, 0.85, 0.96, 0.90 and 22.0, respectively, during the model testing phase. These findings offer valuable insights for enhancing relative humidity prediction models in diverse climatic conditions. The Bagging model?s robust performance underscores its potential application in water resource management. ? 2024 The Authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2655
dc.identifier.doi10.2166/wpt.2024.144
dc.identifier.epage2672
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85201638540
dc.identifier.spage2655
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85201638540&doi=10.2166%2fwpt.2024.144&partnerID=40&md5=7599fe0437cfa4630e17763e96ec88da
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36504
dc.identifier.volume19
dc.pagecount17
dc.publisherIWA Publishingen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleWater Practice and Technology
dc.subjectResource allocation
dc.subjectWater management
dc.subjectEvaporation temperature
dc.subjectIndex values
dc.subjectMean absolute error
dc.subjectMean temperature
dc.subjectModel testing
dc.subjectPan evaporation
dc.subjectPerformance indices
dc.subjectPrediction indices
dc.subjectTesting phase
dc.subjectWater resources management
dc.subjectPrediction models
dc.titleDevelopment of machine learning models for estimation of daily evaporation and mean temperature: a case study in New Delhi, Indiaen_US
dc.typeArticleen_US
dspace.entity.typePublication
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