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Estimation of Spring Maize Evapotranspiration in Semi-Arid Regions of Northeast China Using Machine Learning: An Improved SVR Model Based on PSO and RF Algorithms.
- Source :
- Water (20734441); Apr2023, Vol. 15 Issue 8, p1503, 19p
- Publication Year :
- 2023
-
Abstract
- Accurate estimation of crop evapotranspiration (ET<subscript>c</subscript>) is crucial for effective irrigation and water management. To achieve this, support vector regression (SVR) was applied to estimate the daily ET<subscript>c</subscript> of spring maize. Random forest (RF) as a data pre-processing technique was utilized to determine the optimal input variables for the SVR model. Particle swarm optimization (PSO) was employed to optimize the SVR model. This study used data obtained from field experiments conducted between 2017 and 2019, including crop coefficient and daily meteorological data. The performance of the innovative hybrid RF–SVR–PSO model was evaluated against a standalone SVR model, a back-propagation neural network (BPNN) model and a RF model, using different input meteorological variables. The ET<subscript>c</subscript> values were calculated using the Penman–Monteith equation, which is recommended by the FAO, and used as a reference for the models' estimated values. The results showed that the hybrid RF–SVR–PSO model performed better than all three standalone models for ET<subscript>c</subscript> estimation of spring maize. The Nash–Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R<superscript>2</superscript>) ranges were 0.956–0.958, 0.275–0.282 mm d<superscript>−1</superscript>, 0.221–0.231 mm d<superscript>−1</superscript> and 0.957–0.961, respectively. It is proved that the hybrid RF–SVR–PSO model is appropriate for estimation of daily spring maize ET<subscript>c</subscript> in semi-arid regions. [ABSTRACT FROM AUTHOR]
- Subjects :
- ARID regions
SPRING
MACHINE learning
STANDARD deviations
PARTICLE swarm optimization
Subjects
Details
- Language :
- English
- ISSN :
- 20734441
- Volume :
- 15
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- Water (20734441)
- Publication Type :
- Academic Journal
- Accession number :
- 163460927
- Full Text :
- https://doi.org/10.3390/w15081503