*2.7. Assessment of the Modeling Performance*

This study utilized two metrics to assess the effectiveness of the RF model. The first metric was the coefficient of determination (*R*2, Formula (3)), that indicates the extent to which the independent variable can account for the variability in the dependent variable. The second metric was the root mean square error (RMSE, Formula (4)), that represents the

standard deviation of the difference between the observed data and the fitted model. A higher *R*<sup>2</sup> and a lower RMSE are indicative of a well-fitting model. The model is trained on 60% of the total samples, and the remaining 40% are used for testing. This approach allows for accurate predictions while reducing the risk of over-fitting.

$$R^2 = 1 - \frac{\sum\_{i=1}^{n} (y\_i - \hat{y}\_i)^2}{\sum\_{i=1}^{n} (y\_i - \overline{y})^2} \tag{3}$$

$$\text{RMSE} = \sqrt{\frac{\sum\_{i=1}^{n} (y\_i - \hat{y}\_i)^2}{n}} \tag{4}$$

where *yi* is the measured FSV, *y*ˆ*<sup>i</sup>* is the predicted FSV, *y* is the mean measured FSV, *i* is the same index, and *n* is the number of sample plots.
