Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates
Abstract Improving the accuracy of reference evapotranspiration (RET) estimation is essential for effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The FAO-56 Penman-Monteith (PM-FAO56) model, a widely endorsed approach for RET estimati...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83859-6 |
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| author | Siham Acharki Ali Raza Dinesh Kumar Vishwakarma Mina Amharref Abdes Samed Bernoussi Sudhir Kumar Singh Nadhir Al-Ansari Ahmed Z. Dewidar Ahmed A. Al-Othman Mohamed A. Mattar |
| author_facet | Siham Acharki Ali Raza Dinesh Kumar Vishwakarma Mina Amharref Abdes Samed Bernoussi Sudhir Kumar Singh Nadhir Al-Ansari Ahmed Z. Dewidar Ahmed A. Al-Othman Mohamed A. Mattar |
| author_sort | Siham Acharki |
| collection | DOAJ |
| description | Abstract Improving the accuracy of reference evapotranspiration (RET) estimation is essential for effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The FAO-56 Penman-Monteith (PM-FAO56) model, a widely endorsed approach for RET estimation, often encounters limitations due to the lack of complete meteorological data. This study evaluates the performance of eight empirical models and four machine learning (ML) models, along with their hybrid counterparts, in estimating daily RET within the Gharb and Loukkos irrigated perimeters in Morocco. The ML models examined include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), with hybrid combinations of RF-M5P, RF-XGBoost, RF-LightGBM, and XGBoost-LightGBM. Six input combinations were created, utilizing Tmax, Tmin, RHmean, Rs, and U2, with the PM-FAO56 model serving as the benchmark. Model performance was assessed using four statistical indicators: Kling-Gupta efficiency index (KGE), coefficient of determination (R2), mean squared error (RMSE), and relative root squared error (RRSE). Results indicate that the Valiantzas 2013 (VAL2013b) model outperformed other empirical models across all stations, achieving high KGE and R2 values (0.95–0.97) and low RMSE (0.32–0.35 mm/day) and RRSE (8.14–10.30%). The XGBoost-LightGBM and RF-LightGBM hybrid models exhibited the highest accuracy (average RMSE of 0.015–0.097 mm/day), underscoring the potential of hybrid ML models for RET estimation in subhumid and semi-arid regions, thereby enhancing water resource management and irrigation scheduling. |
| format | Article |
| id | doaj-art-a48903d82a464d6f982d28fa35471588 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-a48903d82a464d6f982d28fa354715882025-01-26T12:33:39ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-024-83859-6Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climatesSiham Acharki0Ali Raza1Dinesh Kumar Vishwakarma2Mina Amharref3Abdes Samed Bernoussi4Sudhir Kumar Singh5Nadhir Al-Ansari6Ahmed Z. Dewidar7Ahmed A. Al-Othman8Mohamed A. Mattar9Faculty of Sciences and Technologies of Tangier, Abdelmalek Essaadi UniversitySchool of Agricultural Engineering, Jiangsu UniversityDepartment of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and TechnologyFaculty of Sciences and Technologies of Tangier, Abdelmalek Essaadi UniversityFaculty of Sciences and Technologies of Tangier, Abdelmalek Essaadi UniversityK. Banerjee Centre of Atmospheric and Ocean Studies, University of AllahabadDepartment of Civil, Environmental, and Natural Resources Engineering, Lulea University of TechnologyPrince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud UniversityDepartment of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud UniversityPrince Sultan Bin Abdulaziz International Prize for Water Chair, Prince Sultan Institute for Environmental, Water and Desert Research, King Saud UniversityAbstract Improving the accuracy of reference evapotranspiration (RET) estimation is essential for effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The FAO-56 Penman-Monteith (PM-FAO56) model, a widely endorsed approach for RET estimation, often encounters limitations due to the lack of complete meteorological data. This study evaluates the performance of eight empirical models and four machine learning (ML) models, along with their hybrid counterparts, in estimating daily RET within the Gharb and Loukkos irrigated perimeters in Morocco. The ML models examined include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), with hybrid combinations of RF-M5P, RF-XGBoost, RF-LightGBM, and XGBoost-LightGBM. Six input combinations were created, utilizing Tmax, Tmin, RHmean, Rs, and U2, with the PM-FAO56 model serving as the benchmark. Model performance was assessed using four statistical indicators: Kling-Gupta efficiency index (KGE), coefficient of determination (R2), mean squared error (RMSE), and relative root squared error (RRSE). Results indicate that the Valiantzas 2013 (VAL2013b) model outperformed other empirical models across all stations, achieving high KGE and R2 values (0.95–0.97) and low RMSE (0.32–0.35 mm/day) and RRSE (8.14–10.30%). The XGBoost-LightGBM and RF-LightGBM hybrid models exhibited the highest accuracy (average RMSE of 0.015–0.097 mm/day), underscoring the potential of hybrid ML models for RET estimation in subhumid and semi-arid regions, thereby enhancing water resource management and irrigation scheduling.https://doi.org/10.1038/s41598-024-83859-6Reference evapotranspirationLight gradient boosting machineHybrid modelFAO-56 Penman-Monteith modelSubhumid and semi-arid zones |
| spellingShingle | Siham Acharki Ali Raza Dinesh Kumar Vishwakarma Mina Amharref Abdes Samed Bernoussi Sudhir Kumar Singh Nadhir Al-Ansari Ahmed Z. Dewidar Ahmed A. Al-Othman Mohamed A. Mattar Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates Scientific Reports Reference evapotranspiration Light gradient boosting machine Hybrid model FAO-56 Penman-Monteith model Subhumid and semi-arid zones |
| title | Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates |
| title_full | Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates |
| title_fullStr | Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates |
| title_full_unstemmed | Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates |
| title_short | Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates |
| title_sort | comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub humid and semi arid climates |
| topic | Reference evapotranspiration Light gradient boosting machine Hybrid model FAO-56 Penman-Monteith model Subhumid and semi-arid zones |
| url | https://doi.org/10.1038/s41598-024-83859-6 |
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