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Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models

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Version 2 2024-06-13, 12:10
Version 1 2019-11-18, 16:53
journal contribution
posted on 2024-06-13, 12:10 authored by ZM Yaseen, AM Al-Juboori, U Beyaztas, N Al-Ansari, KW Chau, C Qi, M Ali, SQ Salih, S Shahid
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 (R2=.92), and with all variables as inputs at Station II (R2=.97). All the ML models performed well in predicting evaporation at the investigated locations.

History

Journal

Engineering applications of computational fluid mechanics

Volume

14

Pagination

70-89

Location

Abingdon, Eng.

ISSN

1994-2060

eISSN

1997-003X

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Issue

1

Publisher

Taylor & Francis