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Application of Decision Tree Algorithm for Predicting Monthly Pan Evaporation Rate

Correlation methods; Decision trees; Forecasting; Meteorology; Water management; Wind; Decision-tree algorithm; Design models; Evaporation rate; Hydrological models; Input parameter; Irrigation system design; Malaysia; Non-linear phenomenon; Pan evaporation; Waters resources; Evaporation

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Bibliographic Details
Main Authors: Abed M., Imteaz M.A., Ahmed A.N., Huang Y.F.
Other Authors: 36612762700
Format: Conference Paper
Published: Engineers Australia 2023
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spelling my.uniten.dspace-270092023-05-29T17:38:37Z Application of Decision Tree Algorithm for Predicting Monthly Pan Evaporation Rate Abed M. Imteaz M.A. Ahmed A.N. Huang Y.F. 36612762700 6506146119 58063046300 55807263900 Correlation methods; Decision trees; Forecasting; Meteorology; Water management; Wind; Decision-tree algorithm; Design models; Evaporation rate; Hydrological models; Input parameter; Irrigation system design; Malaysia; Non-linear phenomenon; Pan evaporation; Waters resources; Evaporation Evaporation is an essential aspect for management of water resources, irrigation system designs, and hydrological modelling. Evaporation is regarded as a complex and nonlinear phenomenon resulting from interactions of multiple climatic factors. This paper presents efficiency of a Decision Tree (DT) machine learning approach in predicting monthly pan evaporation (Ep) through a case study for the Alor Setar region in Malaysia. Daily meteorological data from a weather station in Malaysia 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 2000-2019. Several models were developed by employing various input combinations and other model parameters. To determine the most effective input parameters for the ML model, the Pearson correlation coefficient was used to select the most efficient input parameters (predictors). The developed ML model was compared to Stephens and Stewart, a widely used empirical technique. Model performance was assessed using standard statistical measures. Furthermore, the Taylor diagram was used to assess the accuracy of the investigated model. The findings of the investigations in relation to various performance evaluation criteria show that the proposed DT structure can successfully predict the monthly evaporation rate with a high level of accuracy (R2= 0.946, RMSE=0.220, MAE=0.173, and NSE=0.947). Furthermore, even for the same input sets, the DT model developed in this study outperformed empirical methods and could greatly enhance the accuracy of monthly Ep estimates. � Hydrology and Water Resources Symposium, HWRS 2022. All rights reserved. Final 2023-05-29T09:38:37Z 2023-05-29T09:38:37Z 2022 Conference Paper 2-s2.0-85150600589 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150600589&partnerID=40&md5=b494022b080732379fe1d280a7e50ad3 https://irepository.uniten.edu.my/handle/123456789/27009 434 445 Engineers Australia 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 Correlation methods; Decision trees; Forecasting; Meteorology; Water management; Wind; Decision-tree algorithm; Design models; Evaporation rate; Hydrological models; Input parameter; Irrigation system design; Malaysia; Non-linear phenomenon; Pan evaporation; Waters resources; Evaporation
author2 36612762700
author_facet 36612762700
Abed M.
Imteaz M.A.
Ahmed A.N.
Huang Y.F.
format Conference Paper
author Abed M.
Imteaz M.A.
Ahmed A.N.
Huang Y.F.
spellingShingle Abed M.
Imteaz M.A.
Ahmed A.N.
Huang Y.F.
Application of Decision Tree Algorithm for Predicting Monthly Pan Evaporation Rate
author_sort Abed M.
title Application of Decision Tree Algorithm for Predicting Monthly Pan Evaporation Rate
title_short Application of Decision Tree Algorithm for Predicting Monthly Pan Evaporation Rate
title_full Application of Decision Tree Algorithm for Predicting Monthly Pan Evaporation Rate
title_fullStr Application of Decision Tree Algorithm for Predicting Monthly Pan Evaporation Rate
title_full_unstemmed Application of Decision Tree Algorithm for Predicting Monthly Pan Evaporation Rate
title_sort application of decision tree algorithm for predicting monthly pan evaporation rate
publisher Engineers Australia
publishDate 2023
_version_ 1806427590542491648
score 13.222552

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