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Utilising Machine Learning for Pan Evaporation Prediction - A Case Study in Western Australia

Evaporation has a significant impact on the management of water resources, irrigation system designs, and hydrological modelling due to its complex and nonlinear nature. This is because evaporation is a result of the interactions of various climatic factors. In Australia, research suggests that evap...

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Main Authors:	Abed M., Imteaz M.A., Ahmed A.N.
Other Authors:	36612762700
Format:	Conference Paper
Published:	Newswood Limited 2024
Subjects:	Evaporation Self-Attention Stephens and Stewart Model Transformer Model Climate change Correlation methods Forecasting Machine learning Water management Wind Case-studies Evaporation rate Machine-learning Modeling performance Pan evaporation Self-attention Stephen and stewart model Transformer modeling Waters resources Western Australia
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id	my.uniten.dspace-34493
record_format	dspace
spelling	my.uniten.dspace-344932024-10-14T11:20:10Z Utilising Machine Learning for Pan Evaporation Prediction - A Case Study in Western Australia Abed M. Imteaz M.A. Ahmed A.N. 36612762700 6506146119 57214837520 Evaporation Self-Attention Stephens and Stewart Model Transformer Model Climate change Correlation methods Forecasting Machine learning Water management Wind Case-studies Evaporation rate Machine-learning Modeling performance Pan evaporation Self-attention Stephen and stewart model Transformer modeling Waters resources Western Australia Evaporation Evaporation has a significant impact on the management of water resources, irrigation system designs, and hydrological modelling due to its complex and nonlinear nature. This is because evaporation is a result of the interactions of various climatic factors. In Australia, research suggests that evaporation causes about 40% of the water in open water lakes to be lost each year. Given the potential consequences of climate change, this water loss could become a major issue. This paper presents efficiency of Transformer Neural Network (TNN) approach in predicting monthly pan evaporation (Ep) through a case study in Perth, the capital of Western Australia. Daily meteorological data from a weather station in Perth was deployed for testing and training the model by utilising weather parameters, including maximum temperature, minimum temperature, solar radiation, relative humidity, and wind speed for the period 2009�2022. The Pearson correlation coefficient was used to determine the optimal ML model input parameters. Several models have been developed by combining different input combinations and other model parameters. To evaluate the ML model's performance, it was compared to Stephens and Stewart, a widely used empirical technique. The model's performance was subsequently assessed using standard statistical measures. The results of the performance evaluation criteria suggest that the Transformer model proposed in this study can effectively predict the monthly evaporation rate, benefiting from its self-attention mechanism. The proposed model performed admirably (R2=0.986, RMSE=0.031, MAE=0.025, and NSE=0.987). Additionally, it was demonstrated that the transformer model was more accurate than the empirical method for the same input sets, leading to a notable improvement in the estimation of monthly evaporation rates. � 2023 Newswood Limited. All rights reserved. Final 2024-10-14T03:20:09Z 2024-10-14T03:20:09Z 2023 Conference Paper 2-s2.0-85170522450 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170522450&partnerID=40&md5=d2dbc630ddacc3395ee3669165aaf58d https://irepository.uniten.edu.my/handle/123456789/34493 2245 14 18 Newswood Limited 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/
topic	Evaporation Self-Attention Stephens and Stewart Model Transformer Model Climate change Correlation methods Forecasting Machine learning Water management Wind Case-studies Evaporation rate Machine-learning Modeling performance Pan evaporation Self-attention Stephen and stewart model Transformer modeling Waters resources Western Australia Evaporation
spellingShingle	Evaporation Self-Attention Stephens and Stewart Model Transformer Model Climate change Correlation methods Forecasting Machine learning Water management Wind Case-studies Evaporation rate Machine-learning Modeling performance Pan evaporation Self-attention Stephen and stewart model Transformer modeling Waters resources Western Australia Evaporation Abed M. Imteaz M.A. Ahmed A.N. Utilising Machine Learning for Pan Evaporation Prediction - A Case Study in Western Australia
description	Evaporation has a significant impact on the management of water resources, irrigation system designs, and hydrological modelling due to its complex and nonlinear nature. This is because evaporation is a result of the interactions of various climatic factors. In Australia, research suggests that evaporation causes about 40% of the water in open water lakes to be lost each year. Given the potential consequences of climate change, this water loss could become a major issue. This paper presents efficiency of Transformer Neural Network (TNN) approach in predicting monthly pan evaporation (Ep) through a case study in Perth, the capital of Western Australia. Daily meteorological data from a weather station in Perth was deployed for testing and training the model by utilising weather parameters, including maximum temperature, minimum temperature, solar radiation, relative humidity, and wind speed for the period 2009�2022. The Pearson correlation coefficient was used to determine the optimal ML model input parameters. Several models have been developed by combining different input combinations and other model parameters. To evaluate the ML model's performance, it was compared to Stephens and Stewart, a widely used empirical technique. The model's performance was subsequently assessed using standard statistical measures. The results of the performance evaluation criteria suggest that the Transformer model proposed in this study can effectively predict the monthly evaporation rate, benefiting from its self-attention mechanism. The proposed model performed admirably (R2=0.986, RMSE=0.031, MAE=0.025, and NSE=0.987). Additionally, it was demonstrated that the transformer model was more accurate than the empirical method for the same input sets, leading to a notable improvement in the estimation of monthly evaporation rates. � 2023 Newswood Limited. All rights reserved.
author2	36612762700
author_facet	36612762700 Abed M. Imteaz M.A. Ahmed A.N.
format	Conference Paper
author	Abed M. Imteaz M.A. Ahmed A.N.
author_sort	Abed M.
title	Utilising Machine Learning for Pan Evaporation Prediction - A Case Study in Western Australia
title_short	Utilising Machine Learning for Pan Evaporation Prediction - A Case Study in Western Australia
title_full	Utilising Machine Learning for Pan Evaporation Prediction - A Case Study in Western Australia
title_fullStr	Utilising Machine Learning for Pan Evaporation Prediction - A Case Study in Western Australia
title_full_unstemmed	Utilising Machine Learning for Pan Evaporation Prediction - A Case Study in Western Australia
title_sort	utilising machine learning for pan evaporation prediction - a case study in western australia
publisher	Newswood Limited
publishDate	2024
_version_	1814061122908585984
score	13.211869

Utilising Machine Learning for Pan Evaporation Prediction - A Case Study in Western Australia

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