Publication:
Application of K-nearest neighbors (KNN) technique for predicting monthly pan evaporation

Simple item page
dc.citedby1
dc.contributor.authorAbed M.en_US
dc.contributor.authorImteaz M.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorid36612762700en_US
dc.contributor.authorid6506146119en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55807263900en_US
dc.date.accessioned2024-10-14T03:18:26Z
dc.date.available2024-10-14T03:18:26Z
dc.date.issued2023
dc.description.abstractFinding a reliable computational technique for determining pan evaporation (Ep) may be helpful in the assessment and application of techniques for the development of organic agricultural systems and management of water resources. In this study, monthly evaporation (Ep) was estimated utilising a K-Nearest Neighbours (KNN) model, with monthly climatological statistics obtained from a Malaysian weather station used for training and verifying the model, including climatic factors such as maximum and minimum temperature, mean temperatures, relative humidity, solar radiation, and wind speed, for the period 2000-2019. Several models were devised utilising different combinations of input components and other model factors, and the performance of the devised model was evaluated employing standard statistical indices. The outcomes of evaluation in the testing stage for the final suggested KNN-6 model were R2= 0.946, MAE=0.085, RMSE=0.115, RAE=0.211 and RSE=0.053. The results of the investigations in terms of various performance evaluation criteria highlighted that the proposed KNN structure can model the monthly evaporation losses with reasonable accuracy and thus be used to help local interested parties in discussing the ongoing management of water resources. � 2023 Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo20020
dc.identifier.doi10.1063/5.0131894
dc.identifier.scopus2-s2.0-85160813191
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85160813191&doi=10.1063%2f5.0131894&partnerID=40&md5=b7378937f32e8c12bc31b0ce060d2a23
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34208
dc.identifier.volume2631
dc.publisherAmerican Institute of Physics Inc.en_US
dc.sourceScopus
dc.sourcetitleAIP Conference Proceedings
dc.titleApplication of K-nearest neighbors (KNN) technique for predicting monthly pan evaporationen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS