Academic Journal
Reference Evapotranspiration Estimation Using Genetic Algorithm-Optimized Machine Learning Models and Standardized Penman–Monteith Equation in a Highly Advective Environment
| العنوان: | Reference Evapotranspiration Estimation Using Genetic Algorithm-Optimized Machine Learning Models and Standardized Penman–Monteith Equation in a Highly Advective Environment |
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| المؤلفون: | Kiraga, Shafik, Peters, R. Troy, Molaei, Behnaz, Evett, Steven R., Marek, Gary |
| المصدر: | Agricultural and Environmental Sciences Faculty Research |
| بيانات النشر: | Digital Scholarship @ Tennessee State University |
| سنة النشر: | 2023 |
| المجموعة: | Digital Scholarship @ Tennessee State University |
| مصطلحات موضوعية: | machine learning, genetic algorithm, advective environments, radiation components, aerodynamic components, reference evapotranspiration, Agriculture |
| الوصف: | Accurate estimation of reference evapotranspiration (ETr) is important for irrigation planning, water resource management, and preserving agricultural and forest habitats. The widely used Penman–Monteith equation (ASCE-PM) estimates ETr across various timescales using ground weather station data. However, discrepancies persist between estimated ETr and measured ETr obtained from weighing lysimeters (ETr-lys), particularly in advective environments. This study assessed different machine learning (ML) models in comparison to ASCE-PM for ETr estimation in highly advective conditions. Various variable combinations, representing both radiation and aerodynamic components, were organized for evaluation. Eleven datasets (DT) were created for the daily timescale, while seven were established for hourly and quarter-hourly timescales. ML models were optimized by a genetic algorithm (GA) and included support vector regression (GA-SVR), random forest (GA-RF), artificial neural networks (GA-ANN), and extreme learning machines (GA-ELM). Meteorological data and direct measurements of well-watered alfalfa grown under reference ET conditions obtained from weighing lysimeters and a nearby weather station in Bushland, Texas (1996–1998), were used for training and testing. Model performance was assessed using metrics such as root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R2). ASCE-PM consistently underestimated alfalfa ET across all timescales (above 7.5 mm/day, 0.6 mm/h, and 0.2 mm/h daily, hourly, and quarter-hourly, respectively). On hourly and quarter-hourly timescales, datasets predominantly composed of radiation components or a blend of radiation and aerodynamic components demonstrated superior performance. Conversely, datasets primarily composed of aerodynamic components exhibited enhanced performance on a daily timescale. Overall, GA-ELM outperformed the other models and was thus recommended for ETr estimation at all timescales. The findings emphasize the ... |
| نوع الوثيقة: | text |
| وصف الملف: | application/pdf |
| اللغة: | unknown |
| Relation: | https://digitalscholarship.tnstate.edu/agricultural-and-environmental-sciences-faculty/362; https://digitalscholarship.tnstate.edu/context/agricultural-and-environmental-sciences-faculty/article/1362/viewcontent/water_16_00012.pdf |
| الاتاحة: | https://digitalscholarship.tnstate.edu/agricultural-and-environmental-sciences-faculty/362 https://digitalscholarship.tnstate.edu/context/agricultural-and-environmental-sciences-faculty/article/1362/viewcontent/water_16_00012.pdf |
| رقم الانضمام: | edsbas.4D96A687 |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |