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Improved Prediction of Monthly Pan Evaporation Utilising Support Vector Machine Technique

Neural 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; Evaporatio...

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Main Authors: Abed M., Imteaz M., Ahmed A.N., Huang Y.F.
Other Authors: 36612762700
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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id my.uniten.dspace-26365
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spelling my.uniten.dspace-263652023-05-29T17:09:35Z Improved Prediction of Monthly Pan Evaporation Utilising Support Vector Machine Technique Abed M. Imteaz M. Ahmed A.N. Huang Y.F. 36612762700 6506146119 57214837520 55807263900 Neural 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; Evaporation Evaporation 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. Final 2023-05-29T09:09:35Z 2023-05-29T09:09:35Z 2021 Conference Paper 10.1109/CSDE53843.2021.9718389 2-s2.0-85127914356 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127914356&doi=10.1109%2fCSDE53843.2021.9718389&partnerID=40&md5=a8ed8ac10e457a12ca4cc5799b43f557 https://irepository.uniten.edu.my/handle/123456789/26365 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Neural 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; Evaporation
author2 36612762700
author_facet 36612762700
Abed M.
Imteaz M.
Ahmed A.N.
Huang Y.F.
format Conference Paper
author Abed M.
Imteaz M.
Ahmed A.N.
Huang Y.F.
spellingShingle Abed M.
Imteaz M.
Ahmed A.N.
Huang Y.F.
Improved Prediction of Monthly Pan Evaporation Utilising Support Vector Machine Technique
author_sort Abed M.
title Improved Prediction of Monthly Pan Evaporation Utilising Support Vector Machine Technique
title_short Improved Prediction of Monthly Pan Evaporation Utilising Support Vector Machine Technique
title_full Improved Prediction of Monthly Pan Evaporation Utilising Support Vector Machine Technique
title_fullStr Improved Prediction of Monthly Pan Evaporation Utilising Support Vector Machine Technique
title_full_unstemmed Improved Prediction of Monthly Pan Evaporation Utilising Support Vector Machine Technique
title_sort improved prediction of monthly pan evaporation utilising support vector machine technique
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806425676890243072
score 13.211869

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