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Article

Modeling the Soil Surface Temperature–Wind Speed–Evaporation Relationship Using a Feedforward Backpropagation ANN in Al Medina, Saudi Arabia

by
Samyah Salem Refadah
1,
Sultan AlAbadi
2,
Mansour Almazroui
3,4,
Mohammad Ayaz Khan
5,
Mohamed ElKashouty
6 and
Mohd Yawar Ali Khan
6,*
1
Department of Geography and GIS, Faculty of Arts and Humanities, King Abdulaziz University, Jeddah 21589, Saudi Arabia
2
Hydrogeology and Environmental Geology Program, Saudi Geological Survey, Jeddah 21514, Saudi Arabia
3
Center of Excellence for Climate Change Research/Department of Meteorology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
4
Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK
5
Department of Computer Science, College of Science, Northern Border University, Arar 73213, Saudi Arabia
6
Department of Hydrogeology, Faculty of Earth Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
*
Author to whom correspondence should be addressed.
Technologies 2025, 13(10), 461; https://doi.org/10.3390/technologies13100461
Submission received: 20 August 2025 / Revised: 26 September 2025 / Accepted: 10 October 2025 / Published: 12 October 2025
(This article belongs to the Special Issue Sustainable Water and Environmental Technologies of Global Relevance)
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Abstract

Artificial neural networks (ANNs) offer considerable advantages in predicting evaporation (EVAP), particularly in handling nonlinear relationships and complex interactions among factors like soil surface temperature (SST) and wind speed (WS). In Al Medina, Saudi Arabia, the connections among WS, SST at 5 cm, SST at 10 cm, and EVAP have been modeled using an ANN. This study demonstrates the practical effectiveness and applicability of the approach in simulating complex nonlinear dynamics in real-life systems. The modeling process employs time series data for WS, SST at both 5 cm and 10 cm, and EVAP, gathered from January to December (2002–2010). Four ANNs labeled T1–T4 were developed and trained with the feedforward backpropagation (FFBP) algorithm using MATLAB routines, each featuring a distinct configuration. The networks were further refined through the enumeration technique, ultimately selecting the most efficient network for forecasting EVAP values. The results from the ANN model are compared with the actual measured EVAP values. The mean square error (MSE) values for the optimal network topology are 0.00343, 0.00394, 0.00309, and 0.00306 for T1, T2, T3, and T4, respectively.
Keywords: forecasting; ANNs; evaporation; soil surface temperature; wind speed; Al Medina forecasting; ANNs; evaporation; soil surface temperature; wind speed; Al Medina

Share and Cite

MDPI and ACS Style

Refadah, S.S.; AlAbadi, S.; Almazroui, M.; Khan, M.A.; ElKashouty, M.; Khan, M.Y.A. Modeling the Soil Surface Temperature–Wind Speed–Evaporation Relationship Using a Feedforward Backpropagation ANN in Al Medina, Saudi Arabia. Technologies 2025, 13, 461. https://doi.org/10.3390/technologies13100461

AMA Style

Refadah SS, AlAbadi S, Almazroui M, Khan MA, ElKashouty M, Khan MYA. Modeling the Soil Surface Temperature–Wind Speed–Evaporation Relationship Using a Feedforward Backpropagation ANN in Al Medina, Saudi Arabia. Technologies. 2025; 13(10):461. https://doi.org/10.3390/technologies13100461

Chicago/Turabian Style

Refadah, Samyah Salem, Sultan AlAbadi, Mansour Almazroui, Mohammad Ayaz Khan, Mohamed ElKashouty, and Mohd Yawar Ali Khan. 2025. "Modeling the Soil Surface Temperature–Wind Speed–Evaporation Relationship Using a Feedforward Backpropagation ANN in Al Medina, Saudi Arabia" Technologies 13, no. 10: 461. https://doi.org/10.3390/technologies13100461

APA Style

Refadah, S. S., AlAbadi, S., Almazroui, M., Khan, M. A., ElKashouty, M., & Khan, M. Y. A. (2025). Modeling the Soil Surface Temperature–Wind Speed–Evaporation Relationship Using a Feedforward Backpropagation ANN in Al Medina, Saudi Arabia. Technologies, 13(10), 461. https://doi.org/10.3390/technologies13100461

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