Citation

Bashir, Rab Nawaz and Iqbal, Munawar and Khan, Uzair and Shahid, Muhammad Ali and Rashid, Tahir and Kamal, Shahid and Ng, Kok Why and Rehman, Amjad (2026) Artificial neural network models for evapotranspiration determination using different feature combinations. Asian Journal of Water, Environment and Pollution, 23 (3). 026010003. ISSN 0972-9860

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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.

Item Type:	Article
Uncontrolled Keywords:	Artificial neural networks, Evapotranspiration, Water management, Temperature, Relative humidity, Wind speed
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	31 Jul 2026 07:26
Last Modified:	31 Jul 2026 07:26
URII:	http://shdl.mmu.edu.my/id/eprint/16432

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