Egypt's water future: AI predicts evapotranspiration shifts across climate zones
Study Region: Egypt is a country located in northeastern Africa. Study Focus: The research evaluated the random forest (RF) and extreme gradient boosting (XGB) as single models and the models' hybrid to predict the ETo for the baseline and future (2015–2099) period from Shared Socioeconomic Pat...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581824003173 |
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| author | Ali Mokhtar Mohammed Magdy Hamed Hongming He Ali Salem Zeinab M. Hendy |
| author_facet | Ali Mokhtar Mohammed Magdy Hamed Hongming He Ali Salem Zeinab M. Hendy |
| author_sort | Ali Mokhtar |
| collection | DOAJ |
| description | Study Region: Egypt is a country located in northeastern Africa. Study Focus: The research evaluated the random forest (RF) and extreme gradient boosting (XGB) as single models and the models' hybrid to predict the ETo for the baseline and future (2015–2099) period from Shared Socioeconomic Pathways (SSP1–26, SSP2–45 and SSP5–85) based on 18 GCMs models. New Hydrological Insights for the Region: The hybrid model has performed better than single models; compared RF and XGB to RF-XGB, the RMSE values were decreased in all zones esepically in zone 3 by 16.2 %, these results indicate that the highest performances of all models are observed in the middle and south Egypt, which exhibit the strongest correlation between temperature and ETo. For the SSP5–8.5 scenario, the ETo increased over the years for all zones; the ETo will increase by 4.38 %,3.71 %, 4.27 %, 2.16 %, 3.26 %, 1.35 %, 5.22 % at the year 2099 compared to the year 2015 for zone 1, 2, 3, 4, 5, 6 and 7 respectively. The Tmin and Tmax are the most critical factors that affect the ETo in all zones in the baseline and future scenarios. This study provides important insights into applying machine learning models to estimate ETo and its implications for future water management strategies. Such models hold promise for significantly enhancing regional agricultural water-resource planning and management. |
| format | Article |
| id | doaj-art-9bc3121d6ee34fe281c3f61df98a54ed |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-9bc3121d6ee34fe281c3f61df98a54ed2024-12-08T06:10:22ZengElsevierJournal of Hydrology: Regional Studies2214-58182024-12-0156101968Egypt's water future: AI predicts evapotranspiration shifts across climate zonesAli Mokhtar0Mohammed Magdy Hamed1Hongming He2Ali Salem3Zeinab M. Hendy4School of Geographic Sciences, East China Normal University, Shanghai 209962, China; Department of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza 12613, EgyptConstruction and Building Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport (AASTMT), B 2401 Smart Village, Giza 12577, EgyptSchool of Geographic Sciences, East China Normal University, Shanghai 209962, China; Corresponding author.Civil Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt; Structural Diagnostics and Analysis Research Group, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány ut 2, Pecs H-7624, Hungary; Corresponding author at: Structural Diagnostics and Analysis Research Group, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány ut 2, Pecs H-7624, Hungary.Department of Agricultural Engineering, Faculty of Agriculture, Ain Shams University, Cairo, EgyptStudy Region: Egypt is a country located in northeastern Africa. Study Focus: The research evaluated the random forest (RF) and extreme gradient boosting (XGB) as single models and the models' hybrid to predict the ETo for the baseline and future (2015–2099) period from Shared Socioeconomic Pathways (SSP1–26, SSP2–45 and SSP5–85) based on 18 GCMs models. New Hydrological Insights for the Region: The hybrid model has performed better than single models; compared RF and XGB to RF-XGB, the RMSE values were decreased in all zones esepically in zone 3 by 16.2 %, these results indicate that the highest performances of all models are observed in the middle and south Egypt, which exhibit the strongest correlation between temperature and ETo. For the SSP5–8.5 scenario, the ETo increased over the years for all zones; the ETo will increase by 4.38 %,3.71 %, 4.27 %, 2.16 %, 3.26 %, 1.35 %, 5.22 % at the year 2099 compared to the year 2015 for zone 1, 2, 3, 4, 5, 6 and 7 respectively. The Tmin and Tmax are the most critical factors that affect the ETo in all zones in the baseline and future scenarios. This study provides important insights into applying machine learning models to estimate ETo and its implications for future water management strategies. Such models hold promise for significantly enhancing regional agricultural water-resource planning and management.http://www.sciencedirect.com/science/article/pii/S2214581824003173Crop water requirementWater scarcityClimate change scenariosCMIP6Random ForestHybrid model |
| spellingShingle | Ali Mokhtar Mohammed Magdy Hamed Hongming He Ali Salem Zeinab M. Hendy Egypt's water future: AI predicts evapotranspiration shifts across climate zones Journal of Hydrology: Regional Studies Crop water requirement Water scarcity Climate change scenarios CMIP6 Random Forest Hybrid model |
| title | Egypt's water future: AI predicts evapotranspiration shifts across climate zones |
| title_full | Egypt's water future: AI predicts evapotranspiration shifts across climate zones |
| title_fullStr | Egypt's water future: AI predicts evapotranspiration shifts across climate zones |
| title_full_unstemmed | Egypt's water future: AI predicts evapotranspiration shifts across climate zones |
| title_short | Egypt's water future: AI predicts evapotranspiration shifts across climate zones |
| title_sort | egypt s water future ai predicts evapotranspiration shifts across climate zones |
| topic | Crop water requirement Water scarcity Climate change scenarios CMIP6 Random Forest Hybrid model |
| url | http://www.sciencedirect.com/science/article/pii/S2214581824003173 |
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