## 3 Results
Although the \(t\) variable showed borderline significance, especially for the GB model (_p_ = 0.050), it was generally significant in other models. These results show that machine learning algorithms correctly model the relationships between independent and dependent variables. However, it is seen that some algorithms are more sensitive to specific variables and can capture the effects of these variables to different extents. Pareto charts were obtained for the effect strengths of independent variables on the dependent variable by an ANOVA test performed for the prediction data of the dependent variable from machine learning algorithms. Pareto charts of machine learning algorithms and _Po_ are presented in Figure 3. These charts are presented as Pareto charts created based on the ANOVA test results to evaluate the effect of independent variables on the dependent variable. The first chart (Figure 3a) shows the impact of the independent variables on the actual data selected in the testing phase. In this chart, _Ws_ stands out as the highest effective variable, followed by _Rs_ and _Wd_. Although other variables have a significant effect, their effects are relatively lower. This provides a compatible basis with machine learning models in measuring the impact of independent variables on the dependent variable. Pareto charts (b-f) based on the estimation data of machine learning algorithms show how each model evaluates the effects of independent variables. The graphs (Figure 3b-d) of the RF, kNN, and GB algorithms are primarily similar regarding the impact of independent variables. _Ws_ is again the most potent independent variable in these models, followed by _Rs_ and _Wd_. These models consistently evaluate the importance of independent variables and support accurate prediction capacity. However, the effects of variables such as \(t\) and _Wt_ are lower in these algorithms. When the graphs (Figure 3e,f) of the Tree and LR models are examined, some differences in the effects of independent variables are noticeable. Although the Tree algorithm produces results close to models such as RF and GB regarding the impact of independent variables, it evaluates the effect of the _Nd_ variable relatively weakly. On the other hand, the LR model differs significantly from the different algorithms and evaluates the effects of \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Source** & **PO** & **RF** & **kNN** & **GB** & **LR** & **Tree** \\ \hline Wd (\({}^{\circ}\)) & 0.001 & 0.003 & 0.001 & 0.003 & 0.002 & 0.003 \\ Ws (m/s) & 0.001 & 0.001 & 0.002 & 0.001 & 0.001 & 0.001 \\ Nd (\({}^{\circ}\)) & 0.002 & 0.001 & 0.006 & 0.002 & 0.004 & 0.021 \\ Rs (s\({}^{-1}\)) & 0.001 & 0.001 & 0.002 & 0.007 & 0.030 & 0.001 \\ Wf & 0.001 & 0.021 & 0.001 & 0.001 & 0.001 & 0.010 \\ Wt & 0.002 & 0.002 & 0.003 & 0.001 & 0.020 & 0.004 \\ t (UTC) & 0.013 & 0.003 & 0.001 & 0.050 & 0.006 & 0.001 \\ \hline \end{tabular}
\end{table} 
Table 9: Statistical significance values (_p_-value) on actual data and estimated data selected for the testing phase of independent variables. variables other than _Ws_ at a very low level. This may be due to the linear assumptions of LR, which indicates that the model has a limited capacity to capture complex relationships between independent variables. Pareto charts generally reveal that the effects of independent variables may differ between algorithms, and these evaluations may be critical for model selection. As a result, this study comprehensively evaluated the effects of independent variables on the dependent variable in the light of ANOVA analyses and performance measurements with the estimated data obtained from actual and machine learning algorithms. First, ANOVA analyses performed on the raw data showed that all independent variables had a statistically significant effect. While variables such as _Ws_ and _Rs_ exhibited the strongest  
Figure 3: Pareto charts of accurate data and algorithms selected for the testing phase.