## 4 Results and Discussion
In the meantime, the ExGBT and DL models have revealed reasonable outcomes with \(prec_{n}\) of 96.42% and 96.51%, respectively. However, the TsADL-LULCCD method has exhibited its improved performance with \(prec_{n}\) of 97.88%. Figure 17 represents \(rec_{n}\) inspection of the TsADL-LULCCD model with recent models. The results signified that the SGD and DT approaches had reported worse outcomes with minimum \(rec_{1}\) of 93.1% and 93.56%, respectively. Follow \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Algorithm** & **Epoch** & **Iterations** & **RMSE** & \
\begin{tabular}{l} **Log-Loss** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{l} **Validation** \\ **Loss** \\ \end{tabular} \\ \hline \multirow{3}{*}{\(\mathrm{TsADL-LULCCD}\)} & 10 & 150 & 87.86 & 31.51 & 0.30 \\ \cline{2-6}  & 20 & 300 & 66.76 & 29.88 & 0.32 \\ \cline{2-6}  & 30 & 450 & 45.84 & 28.69 & 0.34 \\ \hline \multirow{3}{*}{\(\mathrm{Stochastic Gradient Descent (SGD)}\)} & 10 & 150 & 88.17 & 31.59 & 0.49 \\ \cline{2-6}  & 20 & 300 & 67.51 & 29.96 & 0.47 \\ \cline{2-6}  & 30 & 450 & 48.04 & 28.77 & 0.46 \\ \hline \multirow{3}{*}{\(\mathrm{Symbolic ML via Genetic Algorithms (GA)}\)} & 10 & 150 & 89.33 & 31.44 & 0.52 \\ \cline{2-6}  & 20 & 300 & 67.93 & 29.80 & 0.53 \\ \cline{1-1} \cline{2-6}  & 30 & 450 & 46.56 & 28.62 & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Training and Validation process results. Figure 16: \(Prec_{n}\) analysis of TsADL-LULCCD approach with other approaches. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Methods** & \(Acc_{y}\) & \(Prec_{n}\) & \(Reca_{1}\) & \(F_{score}\) \\ \hline TsADL-LULCCD & 98.75 & 97.88 & 97.23 & 97.53 \\ Decision Tree (DT) & 91.18 & 91.92 & 93.56 & 91.82 \\ Logistic Regression (LR) & 92.03 & 93.41 & 93.66 & 94.95 \\ Stochastic Gradient Descent (SGD) & 93.11 & 94.06 & 93.10 & 95.08 \\ Extreme Gradient Boosted Trees (ExGBT) & 93.13 & 96.42 & 93.75 & 95.98 \\ Symbolic ML via Genetic Algorithms (GA) & 95.24 & 95.75 & 95.79 & 95.88 \\ Deep Learning (DL) & 97.88 & 96.51 & 95.94 & 96.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of TsADL-LULCCD approach with other approaches. models, which have accomplished certainly closer \(reca_{l}\) of 93.66% and 93.75%, respectively. In the meantime, the GA and DL models have revealed reasonable outcomes with \(reca_{l}\) of 95.79% and 95.94% correspondingly. However, the TSADL-LULCCD method has shown its improved performance with \(reca_{l}\) of 97.23%. Figure 18 denotes \(F_{score}\) investigation of the TSADL-LULCCD model with recent methods. The results signified that the DT and LR approaches had reported worse outcomes with minimum \(F_{score}\) of 91.82% and 94.95%, correspondingly. Followed by the SGD and ExGBT models, which have certainly accomplished closer \(F_{score}\) of 95.08% and 95.88% correspondingly. Meanwhile, the GA and DL methods have revealed reasonable outcomes with \(F_{score}\) of 95.98% and 96.3%, correspondingly. However, the TSADL-LULCCD method has exhibited improved performance with \(F_{score}\) of 97.53%. These results assured the better performance of the TSADL-LULCCD model compared to recent approaches. Figure 17: \(Reca_{l}\) analysis of TSADL-LULCCD approach with other approaches. Figure 18: \(F_{score}\) analysis of TSADL-LULCCD approach with other approaches.