Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models
Article
Article Title | Prediction of evaporation in arid and semi-arid regions: a comparative study using different machine learning models |
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ERA Journal ID | 41540 |
Article Category | Article |
Authors | Yaseen, Zaher Mundher, Al-Juboori, Al-Juboori, Beyaztasc, Ufuk, Al-Ansari, Nadhir, Chau, Kwok-Wing, Qi, Chongchong, Ali, Mumtaz, Salih, Sinan Q. and Shahid, Shamsuddin |
Journal Title | Engineering Applications of Computational Fluid Mechanics |
Journal Citation | 14 (1), pp. 70-89 |
Number of Pages | 20 |
Year | 2020 |
Place of Publication | Hong Kong |
ISSN | 1994-2060 |
1997-003X | |
Digital Object Identifier (DOI) | https://doi.org/10.1080/19942060.2019.1680576 |
Web Address (URL) | https://www.tandfonline.com/doi/full/10.1080/19942060.2019.1680576 |
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 (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. |
Keywords | evaporation; predictive model; machine learning; arid and semi-arid regions; best input combination |
ANZSRC Field of Research 2020 | 461199. Machine learning not elsewhere classified |
Byline Affiliations | University of Technology Malaysia, Malaysia |
University of Mosul, Iraq | |
Bartin University, Turkey | |
Lulea University of Technology, Sweden | |
Hong Kong Polytechnic University, China | |
Central South University, China | |
Deakin University | |
Duy Tan University, Vietnam |
https://research.usq.edu.au/item/w3077/prediction-of-evaporation-in-arid-and-semi-arid-regions-a-comparative-study-using-different-machine-learning-models
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