AccScience Publishing / AJWEP / Online First / DOI: 10.36922/AJWEP026010003
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ORIGINAL RESEARCH ARTICLE

Artificial neural network models for evapotranspiration determination using different feature combinations

Rab Nawaz Bashir1,2,3 ,  Munawar Iqbal3 ,  Uzair Khan3 ,  Muhammad Ali Shahid4 ,  Tahir Rashid3 ,  Shahid Kamal1* ,  Ng Kok Why5 ,  Amjad Rehman2
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1 Center for Advanced Analytics, CoE for Artificial Intelligence, Faculty of Computing and Informatics, Multimedia University Cyberjaya, Selangor, Malaysia
2 Artificial Intelligence and Data Analytics (AIDA) Laboratory, College of Computer & Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
3 Department of Computer Science, Faculty of Information Science, COMSATS University Islamabad Vehari Campus, Vehari, Punjab, Pakistan
4 Department of Computer Science, NAMAL University Mianwali, Mianwali, Punjab, Pakistan
5 Center for Digital Innovations, CoE for Immersive Experience, Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, Malaysia
AJWEP, 026010003 https://doi.org/10.36922/AJWEP026010003
Received: 2 January 2026 | Revised: 9 February 2026 | Accepted: 25 February 2026 | Published online: 7 May 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Evapotranspiration (ET) plays a major role in hydrological management. ET determination is a complex task and is extensively determined using artificial neural network (ANN) models. There is a need to evaluate the potential of ANN models for ET determination using different environmental parameters in an arid region. The study evaluated the ANN models for ET determination with different weather features. A weather dataset from an arid climate in Pakistan was used to assess the performance of ANN models with different feature combinations. The ANN model performed best with four features: daily minimum temperature, daily mean temperature, daily mean relative humidity, and wind speed, compared to models trained with any other combination of features. The ANN model using these four features showed the best performance, with an R2 value of 0.9899, a mean squared error of 0.0617, a mean squared error of 0.2483, and an mean absolute error of 0.1917 mm day−1. Temperature appeared to be the most significant feature for ET determination using the ANN model at the selected location. The proposed model has potential applications in precision agriculture under arid conditions for effective drought management.

Keywords
Artificial neural networks
Evapotranspiration
Water management
Temperature
Relative humidity
Wind speed
Funding
This research is supported by Multimedia University (MMU) through its Article Page Charge (APC) Sponsorship Scheme.
Conflict of interest
The authors declare no conflict of interest.
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Asian Journal of Water, Environment and Pollution, Electronic ISSN: 1875-8568 Print ISSN: 0972-9860, Published by AccScience Publishing
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