Prediction of evaporation in arid and semi-arid regions

a comparative study using different machine learning models

Document identifier:
Access full text here:10.1080/19942060.2019.1680576
Keyword: Engineering and Technology, Civil Engineering, Geotechnical Engineering, Teknik och teknologier, Samhällsbyggnadsteknik, Geoteknik, Evaporation, Predictive model, Machine learning, Arid and semi-arid regions, Best input combination, Soil Mechanics
Publication year: 2020
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructure
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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.


Zaher Mundher Yaseen

School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Skudai, Johor Bahru, Malaysia
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Anas Mahmood Al-Juboor

Dams and Water Resources Research Center, University of Mosul, Mosul, Iraq
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Ufuk Beyaztas

Department of Statistics, Bartin University, Bartin, Turkey
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Nadhir Al-Ansari

Luleå tekniska universitet; Geoteknologi
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Kwok-Wing Chaue

Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, People’s Republic of China
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Chongchong Qi

School of Resources and Safety Engineering, Central South University, Changsha, Hunan Province, People’s Republic of China
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Mumtaz Ali

Deakin-SWU Joint Research Centre on Big Data, School of Information Technology, Deakin University, Australia
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Sinan Q. Salih

Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
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Shamsuddin Shahid

School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Skudai, Johor Bahru, Malaysia
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