Table 1.
Pearson correlation coefficients between daily evapotranspiration (ETo) and meteorological data
| SR | Tavg | Tmax | RH | U2 | Ra | VPD | #D | |
|---|---|---|---|---|---|---|---|---|
| ETo | 0.94 | 0.66 | 0.72 | −0.69 | 0.05 | 0.62 | 0.82 | −0.41 |
[i] SR – average daily level of solar radiation, Tmax – maximum daily air temperature, Tavg – mean daily temperature, RH – mean air relative humidity, U2 – wind speed at 2 m height, Ra – extraterrestrial solar radiation, VPD – vapor pressure deficit, #D – day number of the year
Figure 1.
Relationship between evapotranspiration (ETo) and important weather parameters (Skierniewice 2009–2022)
Figure 2.
The importance of variables when creating regression trees
SR – average solar radiation level, VPD – vapor pressure deficit, Tmax – maximum temperature, Tavg – average temperature, RH – relative humidity, Ra – extraterrestrial solar radiation, #D – day number of the year
Figure 3.
The importance of variables when creating boosted trees
Note: see Figure 2
Figure 4.
The importance of variables when creating random forests
Note: see Figure 2
Figure 5.
Net changes in extraterrestrial solar radiation (Ra) and the average level of solar radiation (SR) during the vegetation period (Skierniewice 2009–2022)
Table 2.
Statistical analysis of the performance of the RT, BRT, RF, and ANN models in estimating daily ETo with two different meteorological input datasets
| Model | Radiation | R2 | Slope | MSE | RMSE |
|---|---|---|---|---|---|
| Regression trees | + | 0.911 | 0.911 | 0.108 | 0.329 |
| − | 0.813 | 0.813 | 0.228 | 0.478 | |
| Boosted trees | + | 0.942 | 0.931 | 0.073 | 0.269 |
| − | 0.834 | 0.825 | 0.205 | 0.453 | |
| Random forests | + | 0.952 | 0.895 | 0.066 | 0.256 |
| − | 0.841 | 0.799 | 0.207 | 0.455 | |
| Artificial neural networks | + | 0.963 | 0.947 | 0.023 | 0.152 |
| − | 0.870 | 0.843 | 0.082 | 0.286 |
[i] R2 – determination coefficient, MSE – mean squared error, RMSE – root mean square error