Machine Learning Applications for Evaporation Predictions From Small Reservoirs: Potential Water Savings for Lower Rio Grande Valley, Texas
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| izdano v: | ProQuest Dissertations and Theses (2025) |
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ProQuest Dissertations & Theses
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| Online dostop: | Citation/Abstract Full Text - PDF |
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| 100 | 1 | |a Abdullah, Syed Muhammad Fahad | |
| 245 | 1 | |a Machine Learning Applications for Evaporation Predictions From Small Reservoirs: Potential Water Savings for Lower Rio Grande Valley, Texas | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a 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. | |
| 653 | |a Environmental engineering | ||
| 653 | |a Hydrologic sciences | ||
| 653 | |a Water resources management | ||
| 653 | |a Computer science | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275325910/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275325910/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |