## 3 Methodology
### Atmospheric Duct Interference Prediction
It used ReLU and SoftMax activations across its layers, along with the Adam optimizer and MSE loss. In addition, a Stochastic Gradient Descent (SGD) classifier was implemented using 18 features. This model also employed MSE loss and varied the learning rate between 0.001 and 0.048, consistent with the other models. The parameters of the prediction model are given in Table 6.  
This study further explored a Gradient Boosting (GB) classifier and an Extreme Gradient Boosting (XGBoost) model. Both utilized 18 features and were trained under the two scenario setups. They were optimized using different learning rates and evaluated using the same classification and validation metrics. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**SGD Classifier**} & **Gradient Boosting Classifier** & \multicolumn{2}{c}{**Optimized Distributed Gradient Boosting Classifier**} \\ \hline \hline \multicolumn{1}{c}{Scaler} & Standard Scaler & Min-Max Scaler & Min-Max Scaler \\ \hline \hline \multicolumn{1}{c}{Algorithm} & SVM: Linear & Random Forest & Random Forest \\ \hline \hline \multicolumn{1}{c}{Dataset shuffled} & Yes & Yes & Yes \\ \hline \hline \multicolumn{1}{c}{Estimators} & - & 100 & 100 \\ \hline \multicolumn{1}{c}{Max-Depth} & - & 2 & 2 \\ \hline \multicolumn{1}{c}{Max-Features} & - & 2 & 2 \\ \hline \hline \multicolumn{1}{c}{Loss} & MSE & MSE & MSE \\ \hline \hline \multicolumn{1}{c}{Iterations} & 1000 & - & - \\ \hline \hline \multicolumn{1}{c}{Kernel} & Linear & - & - \\ \hline \hline \multicolumn{1}{c}{Other features} & Macro-average & Macro-average & Macro-average \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The parameters of the SGD, GB, and XGB classifiers. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Base Station**} & **Longitude** & **Latitude** \\ \hline \hline \multicolumn{1}{c}{Palali} & 80.08 & 9.79 \\ \hline \hline \multicolumn{1}{c}{Karianagar} & 79.86 & 9.71 \\ \hline \multicolumn{1}{c}{Kandarodai} & 80.01 & 9.75 \\ \hline \hline \multicolumn{1}{c}{Jaffna} & 80.00 & 9.66 \\ \hline \hline \multicolumn{1}{c}{Manipai} & 79.99 & 9.72 \\ \hline \hline \multicolumn{1}{c}{Alaweddy} & 80.01 & 9.77 \\ \hline \hline \multicolumn{1}{c}{Kankasanthure} & 80.03 & 9.81 \\ \hline \hline \multicolumn{1}{c}{Nallur} & 80.03 & 9.67 \\ \hline \hline \multicolumn{1}{c}{Chawakachcheri} & 80.16 & 9.65 \\ \hline \hline \multicolumn{1}{c}{Kodikamam} & 80.22 & 9.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The coordinates of the base stations in the Jaffna district [7]. Finally, a cascaded ML-DL hybrid model was constructed to integrate the strengths of both traditional and deep learning techniques. This model's architecture is illustrated in Figure 1 and detailed in Table 7. It follows the same two-scenario framework and utilizes adaptive learning rates, MSE loss, and a combination of model components for enhanced performance. The dataset contains interference values for the 12 subcarriers of the zeroth physical resource block of the TD-LTE network. Atmospheric duct interference prediction is performed individually in each subcarrier of the physical resource block (i.e., physical resource block 0). One physical resource contains 12 consecutive subcarriers in the TD-LTE systems. The evaluation parameters in the Results and Discussion Section are obtained for the first subcarrier of the zeroth physical resource block. Similarly, we have collected the evaluation parameters for the other subcarriers in the zeroth physical resource block.