Machine Learning Applications for Evaporation Predictions From Small Reservoirs: Potential Water Savings for Lower Rio Grande Valley, Texas
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| Publicado en: | ProQuest Dissertations and Theses (2025) |
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ProQuest Dissertations & Theses
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| Resumen: | Local-scale reservoirs are important to regional water balance, but these are often overlooked. This study presents a robust machine learning (ML) approach leveraging reanalysis datasets to estimate daily evaporation for local-scale reservoirs in semi-arid South Texas. Selected models were trained with daily lake evaporation model (DLEM) estimates and used climatic and reservoir-specific properties as feature input variables. The multi-reservoirs training approach ensured applicable model generalization. Results show promising predictive performance with R² values ranging from 0.55–0.67 (testing) and 0.64–0.78 (validation), NSE values ranged from 0.54 0.67 (testing) and 0.64–0.78 (validation), and RMSE values ranged between 1.52–1.80 mm/day (testing) and 1.22–1.58 mm/day (validation). The findings highlight potential water savings of up to 2.1×105 ac-ft per year, which is equivalent to ~8% of the capacity of one major regional reservoir, if floating solar photovoltaic (PV) is deployed to cover 30% of its surface. |
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| ISBN: | 9798265410818 |
| Fuente: | ProQuest Dissertations & Theses Global |