Publication:
Improved Prediction of Monthly Pan Evaporation Utilising Support Vector Machine Technique

Simple item page
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.accessioned2023-05-29T09:09:35Z
dc.date.available2023-05-29T09:09:35Z
dc.date.issued2021
dc.descriptionNeural networks; Support vector machines; Water management; Wind; Hydrological models; Irrigation system design; Key elements; Malaysians; Pan evaporation; Resource management models; Support vector machine techniques; Support vectors machine; Water resources management; Weather stations; Evaporationen_US
dc.description.abstractEvaporation is a key element for irrigation system design, water resource management, and hydrological modelling. In this research work, monthly evaporation (Ep) was projected by utilising Support Vector Machine (SVM). Monthly meteorological statistics from a Malaysian weather station were utilised for training and testing the model by employing climatic aspects, such as mean temperature, minimum temperature, maximum temperature, wind speed, relative humidity, and solar radiation for the period 2000 to 2019. Various models were formulated by utilising diverse combination of input elements and other model parameters. The performance of the formulated model was assessed by utilising standard statistical indices. The outcomes indicated that the developed SVM model can significantly improve the accuracy of monthly Ep projections. The achieved performance measures are, R2= 0.970, MAE=0.067, MSE=0.007, RMSE=0.087, RAE=0.163 and RSE=0.029. � IEEE 2022.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/CSDE53843.2021.9718389
dc.identifier.scopus2-s2.0-85127914356
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85127914356&doi=10.1109%2fCSDE53843.2021.9718389&partnerID=40&md5=a8ed8ac10e457a12ca4cc5799b43f557
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26365
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021
dc.titleImproved Prediction of Monthly Pan Evaporation Utilising Support Vector Machine Techniqueen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS