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Use of gene expression programming to predict reference evapotranspiration in different climatic conditions
School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, People’s Republic of China; Department of Geology and Geophysics, College of Science, King Saud University, P.O. Box 11451 Riyadh, Saudi Arabia.ORCID iD: 0000-0001-9207-5779
Department of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand 263145, India.ORCID iD: 0000-0002-2421-6995
Department of Earth Sciences, Faculty of Sciences and Technologies of Tangier (FSTT), Abdelmalek Essaadi University, Tétouan 93000, Morocco.ORCID iD: 0000-0001-9421-0807
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.ORCID iD: 0000-0002-6790-2653
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2024 (English)In: Applied water science, ISSN 2190-5487, E-ISSN 2190-5495, Vol. 14, article id 152Article in journal (Refereed) Published
Abstract [en]

Evapotranspiration plays a pivotal role in the hydrological cycle. It is essential to develop an accurate computational model for predicting reference evapotranspiration (RET) for agricultural and hydrological applications, especially for the management of irrigation systems, allocation of water resources, assessments of utilization and demand and water use allocations in rural and urban areas. The limitation of climatic data to estimate RET restricted the use of standard Penman–Monteith method recommended by food and agriculture organization (FAO-PM56). Therefore, the current study used climatic data such as minimum, maximum and mean air temperature (Tmax, Tmin, Tmean), mean relative humidity (RHmean), wind speed (U) and sunshine hours (N) to predict RET using gene expression programming (GEP) technique. In this study, a total of 17 different input meteorological combinations were used to develop RET models. The obtained results of each GEP model are compared with FAO-PM56 to evaluate its performance in both training and testing periods. The GEP-13 model (Tmax, Tmin, RHmean, U) showed the lowest errors (RMSE, MAE) and highest efficiencies (R2, NSE) in semi-arid (Faisalabad and Peshawar) and humid (Skardu) conditions while GEP-11 and GEP-12 perform best in arid (Multan, Jacobabad) conditions during training period. However, GEP-11 in Multan and Jacobabad, GEP-7 in Faisalabad, GEP-1 in Peshawar, GEP-13 in Islamabad and Skardu outperformed in testing  period. In testing phase, the GEP models R2 values reach 0.99, RMSE values ranged from 0.27 to 2.65, MAE values from 0.21 to 1.85 and NSE values from 0.18 to 0.99. The study findings indicate that GEP is effective in predicting RET when there are minimal climatic data. Additionally, the mean relative humidity was identified as the most relevant factor across all climatic conditions. The findings of this study may be used to the planning and management of water resources in practical situations, as they demonstrate the impact of input variables on the RET associated with different climatic conditions.

Place, publisher, year, edition, pages
Springer Nature, 2024. Vol. 14, article id 152
Keywords [en]
Gene expression programming, Reference evapotranspiration, Climatic regions, Penman–Monteith method, Machine learning, GEP models, Environment of Pakistan
National Category
Oceanography, Hydrology and Water Resources Climate Research
Research subject
Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-106129DOI: 10.1007/s13201-024-02200-8OAI: oai:DiVA.org:ltu-106129DiVA, id: diva2:1867039
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Full text license: CC BY 4.0;

Funder: King Saud University, Riyadh, Saudi Arabia (RSP2024R327);

Available from: 2024-06-10 Created: 2024-06-10 Last updated: 2024-06-10

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Al-Ansari, Nadhir

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2627282930313229 of 79
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