Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81781
Title: Prediction of evaporation in arid and semi-arid regions : a comparative study using different machine learning models
Authors: Yaseen, ZM
Al-Juboori, AM
Beyaztas, U
Al-Ansari, N
Chau, KW 
Qi, CC
Ali, M
Salih, SQ
Shahid, S
Issue Date: 2020
Source: Engineering applications of computational fluid mechanics, 2020, v. 14, no. 1, p. 70-89
Abstract: Evaporation, one of the fundamental components of the hydrology cycle, is differently influenced by various meteorological variables in different climatic regions. The accurate prediction of evaporation is essential for multiple water resources engineering applications, particularly in developing countries like Iraq where the meteorological stations are not sustained and operated appropriately for in situ estimations. This is where advanced methodologies such as machine learning (ML) models can make valuable contributions. In this research, evaporation is predicted at two different meteorological stations located in arid and semi-arid regions of Iraq. Four different ML models for the prediction of evaporation - the classification and regression tree (CART), the cascade correlation neural network (CCNNs), gene expression programming (GEP), and the support vector machine (SVM) - were developed and constructed using various input combinations of meteorological variables. The results reveal that the best predictions are achieved by incorporating sunshine hours, wind speed, relative humidity, rainfall, and the minimum, mean, and maximum temperatures. The SVM was found to show the best performance with wind speed, rainfall, and relative humidity as inputs at Station I (R-2 = .92), and with all variables as inputs at Station II (R-2 = .97). All the ML models performed well in predicting evaporation at the investigated locations.
Keywords: Evaporation
Predictive model
Machine learning
Arid and semi-arid regions
Best input combination
Publisher: Taylor & Francis
Journal: Engineering applications of computational fluid mechanics 
ISSN: 1994-2060
EISSN: 1997-003X
DOI: 10.1080/19942060.2019.1680576
Rights: © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Yaseen, Z. M., Al-Juboori, A. M., Beyaztas, U., Al-Ansari, N., Chau, K. W., Qi, C. C., . . . Shahid, S. (2020). Prediction of evaporation in arid and semi-arid regions : a comparative study using different machine learning models. Engineering Applications of Computational Fluid Mechanics, 14(1), 70-89 is available at https://dx.doi.org/10.1080/19942060.2019.1680576
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