## 4 Results and Discussion
### The Results of the ADI Prediction Models
#### 4.1.2 The Evaluation Parameters of the ML and DL Classifier-Based Prediction Model  
The evaluation parameters of the ML and DL classifier-based prediction models are given in Table 17. The results are given for both Scenario One and Scenario Two.  
The evaluation results of the machine learning (ML) and deep learning (DL) classifier-based models under both scenarios reveal key insights into their predictive performance for atmospheric duct interference. In Scenario One, where data are collected from all ten base stations, the Gradient Boosting Classifier (GBC) and Optimized Distributed Gradient Boosting (XGB) models achieved the highest test accuracy of 0.77, with XGB showing better overall balance across precision (0.79), recall (0.61), and F1 score (0.63), along with a relatively low mean squared error (MSE) of 0.26. The CNN classifier also performed well, achieving a test accuracy of 0.75 and the lowest MSE of 0.11, although its F1 score (0.30) was comparatively lower.  
In Scenario Two, which uses data from a single base station, the Stochastic Gradient Descent (SGD) model showed the most improvement, increasing its test accuracy to 0.85, precision to 0.79, and F1 score to 0.74, with an MSE of 0.25. The CNN classifier again demonstrated strong performance, matching its Scenario One accuracy (0.77), while achie  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1 Score**} \\ \hline ML models & Train & Test & Train & Test & Train & Test & Train & Test \\ \hline KNN [8] & - & 0.670 & - & - & - & - & - & - \\ \hline SVM [8] & - & 0.650 & - & - & - & - & - & - \\ \hline SVM linear & 0.635 & 0.634 & 0.390 & 0.387 & 0.355 & 0.355 & 0.302 & 0.301 \\ \hline SVM rbf & 0.686 & 0.672 & 0.665 & 0.623 & 0.485 & 0.463 & 0.508 & 0.478 \\ \hline SVM polynomial & 0.677 & 0.668 & 0.706 & 0.682 & 0.467 & 0.451 & 0.487 & 0.465 \\ \hline SVM sigmoid & 0.524 & 0.522 & 0.302 & 0.301 & 0.302 & 0.301 & 0.278 & 0.278 \\ \hline Random forest [13] & - & 0.650 & - & - & - & - & - & - \\ \hline Random forest M1 & 0.999 & 0.723 & 0.999 & 0.657 & 0.999 & 0.573 & 0.999 & 0.594 \\ \hline Random forest M2 & 0.999 & 0.721 & 0.999 & 0.636 & 0.999 & 0.566 & 0.999 & 0.593 \\ \hline LSTM [26] & - & 0.984 & - & - & - & - & - \\ \hline LSTM & 0.636 & 0.574 & 0.477 & 0.413 & 0.432 & 0.443 & 0.412 & 0.342 \\ \hline CNN [15] & - & 0.856 & - & - & - & - & - & - \\ \hline CNN [17] & 0.990 & 0.977 & - & - & - & - & - & - \\ \hline CNN & 0.655 & 0.653 & 0.562 & 0.562 & 0.456 & 0.450 & 0.464 & 0.455 \\ \hline \hline \end{tabular}
\end{table}
Table 16: The evaluation parameters of the ML and DL models, with a comparison of the state of the art.  
ing the highest precision (0.83) and recall (0.77) across all models in this scenario and the lowest MSE of 0.09.  
Overall, CNN and Gradient Boosting-based models consistently showed robust performance in both scenarios, while the SGD classifier significantly improved with localized data. These results suggest that model effectiveness varies with data granularity, and that CNN classifiers in particular offer high precision and efficiency even with limited input data.