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

Zaher Mundher Yaseen, Anas Mahmood Al-Juboori, Ufuk Beyaztas, Nadhir Al-Ansari, Kwok Wing Chau, Chongchong Qi, Mumtaz Ali, Sinan Q. Salih, Shamsuddin Shahid

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)70-89
Number of pages20
JournalEngineering Applications of Computational Fluid Mechanics
Volume14
Issue number1
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

  • arid and semi-arid regions
  • best input combination
  • evaporation
  • machine learning
  • predictive model

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

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