Predictive modeling of terrestrial radiation for optimizing solar-powered irrigation under limited data in Adrar region (Algeria)

Authors

  • Assia Meziani New Technology and Local Development Laboratory, Department of Hydraulic and Civil Engineering, Faculty of Technology, University of El-Oued, El-Oued BP 789, Algeria https://orcid.org/0000-0002-2569-1552
  • Nabil Mega New Technology and Local Development Laboratory, Department of Hydraulic and Civil Engineering, Faculty of Technology, University of El-Oued, El-Oued BP 789, Algeria https://orcid.org/0000-0002-4450-6658
  • Abdelmonen Miloudi New Technology and Local Development Laboratory, Department of Hydraulic and Civil Engineering, Faculty of Technology, University of El-Oued, El-Oued BP 789, Algeria https://orcid.org/0000-0003-3490-1168
  • António Canatário Duarte School of Agriculture, Instituto Politécnico De Castelo Branco (IPCB), 6001-909 Castelo Branco, Portugal; Research Center for Natural Resources, Environment, and Society, Instituto Politécnico De Castelo Branco (IPCB), 6001-909 Castelo Branco, Portugal; Research Center of Geobiosciences, Geoengineering, and Geotechnologies, University of Beira Interior, 6201-001 Covilhã, Portugal https://orcid.org/0000-0002-0319-378X
Article ID: 783
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DOI:

https://doi.org/10.18686/cest783

Keywords:

terrestrial radiation; machine learning; random forest; solar irrigation; Adrar region; hyper arid agriculture; renewable energy

Abstract

An accurate estimation of the radiation is a prerequisite for the net radiation balance, evapotranspiration modeling, and optimal scheduling of water extraction by solar-powered irrigation, especially in the water-scarce Saharan zone. In this study, we developed a predictive model to estimate daily terrestrial radiation at the surface of the Adrar region in Algeria. We used a training set of 25 years of data (2000–2025) from 10 stations (Adrar, Tamantit, Sidi Ahmed Timmi, Fenoughil, Zaouiet Kounta, Reggane, Gharmianou, Tittaf, Ikiss, Kassbet Lahrar) with only three features: soil temperature (0–7 cm), air temperature (2 m), and vapor pressure deficit. The robustness of the models was ensured by a time-based split. The random forest (RF), gradient boosting (GB), and extra trees (ET) tree-based models were evaluated on the generalization set. RF and ET exhibited the best performance (R = 0.92, rootean square error—RMSE = 31.85 W/m2, Nash–Sutcliffe efficiency—NSE = 0.84 for RF; R = 0.92, RMSE = 32.33 W/m2, NSE = 0.84 for ET), whereas GB exhibited poor performance (R = 0.90, RMSE = 36.67 W/m2, NSE = 0.79). Finally, we proposed a map for solar-powered irrigation optimization. In particular, we demonstrated that the southern part of the Adrar region (Reggane, Zaouiet Kounta) has high potential for solar-powered irrigation (more than 350 W/m2). This study contributes to hyper-arid agricultural regions in Algeria through water conservation and the utilization of renewable energy.

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Published

2026-06-03

How to Cite

Meziani, A., Mega, N., Miloudi, A., & Canatário Duarte, A. (2026). Predictive modeling of terrestrial radiation for optimizing solar-powered irrigation under limited data in Adrar region (Algeria). Clean Energy Science and Technology, 4(3). https://doi.org/10.18686/cest783

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Vol. 4 No. 3 (2026): In progress

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Copyright (c) 2026 Assia Meziani, Nabil Mega, Abdelmonen Miloudi, António Canatário Duarte

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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