Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece
The aim of this study was to investigate the utility of multiple linear regression (MLR) for the estimation of reference evapotranspiration (ETo) of the Peloponnese, Greece, for two representative months of winter and summer during 2016–2019. Another objective was to test the number of inputs needed...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Hydrology |
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| Online Access: | https://www.mdpi.com/2306-5338/9/7/124 |
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| author | Stavroula Dimitriadou Konstantinos G. Nikolakopoulos |
| author_facet | Stavroula Dimitriadou Konstantinos G. Nikolakopoulos |
| author_sort | Stavroula Dimitriadou |
| collection | DOAJ |
| description | The aim of this study was to investigate the utility of multiple linear regression (MLR) for the estimation of reference evapotranspiration (ETo) of the Peloponnese, Greece, for two representative months of winter and summer during 2016–2019. Another objective was to test the number of inputs needed for satisfactorily accurate estimates via MLR. Datasets from sixty-two meteorological stations were exploited. The available independent variables were sunshine hours (N), mean temperature (Tmean), solar radiation (Rs), net radiation (Rn), wind speed (u<sub>2</sub>), vapour pressure deficit (es − ea), and altitude (Z). Sixteen MLR models were tested and compared to the corresponding ETo estimates computed by FAO-56 Penman–Monteith (FAO PM) in a previous study, via statistical indices of error and agreement. The MLR5 model with five input variables outperformed the other models (RMSE = 0.28 mm d<sup>−1</sup>, adj. R<sup>2</sup> = 98.1%). Half of the tested models (two to six inputs) exhibited very satisfactory predictions. Models of one input (e.g., N, Rn) were also promising. However, the MLR with u<sub>2</sub> as the sole input variable presented the worst performance, probably because its relationship with ETo cannot be linearly described. The results indicate that MLR has the potential to produce very good predictive models of ETo for the Peloponnese, based on the literature standards. |
| first_indexed | 2024-03-09T10:18:16Z |
| format | Article |
| id | doaj.art-611678ac06cb48d3881df61dfb0b3f73 |
| institution | Directory Open Access Journal |
| issn | 2306-5338 |
| language | English |
| last_indexed | 2024-03-09T10:18:16Z |
| publishDate | 2022-07-01 |
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| series | Hydrology |
| spelling | doaj.art-611678ac06cb48d3881df61dfb0b3f732023-12-01T22:13:23ZengMDPI AGHydrology2306-53382022-07-019712410.3390/hydrology9070124Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, GreeceStavroula Dimitriadou0Konstantinos G. Nikolakopoulos1Department of Geology, University of Patras, 26504 Patras, GreeceDepartment of Geology, University of Patras, 26504 Patras, GreeceThe aim of this study was to investigate the utility of multiple linear regression (MLR) for the estimation of reference evapotranspiration (ETo) of the Peloponnese, Greece, for two representative months of winter and summer during 2016–2019. Another objective was to test the number of inputs needed for satisfactorily accurate estimates via MLR. Datasets from sixty-two meteorological stations were exploited. The available independent variables were sunshine hours (N), mean temperature (Tmean), solar radiation (Rs), net radiation (Rn), wind speed (u<sub>2</sub>), vapour pressure deficit (es − ea), and altitude (Z). Sixteen MLR models were tested and compared to the corresponding ETo estimates computed by FAO-56 Penman–Monteith (FAO PM) in a previous study, via statistical indices of error and agreement. The MLR5 model with five input variables outperformed the other models (RMSE = 0.28 mm d<sup>−1</sup>, adj. R<sup>2</sup> = 98.1%). Half of the tested models (two to six inputs) exhibited very satisfactory predictions. Models of one input (e.g., N, Rn) were also promising. However, the MLR with u<sub>2</sub> as the sole input variable presented the worst performance, probably because its relationship with ETo cannot be linearly described. The results indicate that MLR has the potential to produce very good predictive models of ETo for the Peloponnese, based on the literature standards.https://www.mdpi.com/2306-5338/9/7/124multiple linear regressionlinear regressionFAO Penman–Monteithreference evapotranspirationPeloponneseGreece |
| spellingShingle | Stavroula Dimitriadou Konstantinos G. Nikolakopoulos Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece Hydrology multiple linear regression linear regression FAO Penman–Monteith reference evapotranspiration Peloponnese Greece |
| title | Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece |
| title_full | Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece |
| title_fullStr | Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece |
| title_full_unstemmed | Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece |
| title_short | Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece |
| title_sort | multiple linear regression models with limited data for the prediction of reference evapotranspiration of the peloponnese greece |
| topic | multiple linear regression linear regression FAO Penman–Monteith reference evapotranspiration Peloponnese Greece |
| url | https://www.mdpi.com/2306-5338/9/7/124 |
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