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

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

Acknowledgments

The authors would like to acknowledge the data source providers, the Meteorological Organization of Seismology (MOS) and the Ministry of Agriculture and Water Resources of Iraq. The editors and reviewers are very much appreciated for their constructive comments.

Disclosure statement

No potential conflict of interest was reported by the authors.