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Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models
School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Skudai, Johor Bahru, Malaysia.
Dams and Water Resources Research Center, University of Mosul, Mosul, Iraq;.
Department of Statistics, Bartin University, Bartin, Turkey.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.ORCID iD: 0000-0002-6790-2653
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2020 (English)In: Engineering Applications of Computational Fluid Mechanics, ISSN 1994-2060, E-ISSN 1997-003X, Vol. 14, no 1, p. 70-89Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Taylor & Francis, 2020. Vol. 14, no 1, p. 70-89
Keywords [en]
evaporation, predictive model, machine learning, arid and semi-arid regions, best input combination
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-76842DOI: 10.1080/19942060.2019.1680576ISI: 000496623500001Scopus ID: 2-s2.0-85075131156OAI: oai:DiVA.org:ltu-76842DiVA, id: diva2:1372629
Note

Validerad;2019;Nivå 2;2019-11-25 (johcin)

Available from: 2019-11-25 Created: 2019-11-25 Last updated: 2019-11-29Bibliographically approved

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Al-Ansari, Nadhir

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