## 4 Proposed LU/LC Prediction Using Vision Transformer-Based Bi-LSTM Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**LU/LC Class**} & \multicolumn{6}{c}{**Area (ha)**} \\ \cline{2-7}  & \multicolumn{6}{c}{**Year**} \\ \cline{2-7}  & **2012** & **2015** & **2018** & **2021** & **2024** & **2027** \\ \hline High Vegetation & 1651.04 & 1601.22 & 1621.18 & 1596.04 & 1568.23 & 1553.17 \\ Less Vegetation & 736.85 & 786.67 & 766.71 & 791.85 & 819.66 & 834.72 \\ Total (ha) & 2387.89 & 2387.89 & 2387.89 & 2387.89 & 2387.89 & 2387.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: LU/LC area statistics for LU/LC Map (2012–2027). ## 6 Comparative Analysis

In this research work, we have proposed the Vision Transformer-based Bi-LSTM prediction model for analyzing the past, present, and future changes of Javadi Hills, India. We also infer that the LU/LC prediction accuracy of our model provides a lower error rate, i.e., below 5%. From the thorough analysis, we infer that the use of the LST map has a high impact on the LU/LC environment, and it was considered an important spatial feature for the prediction of the LU/LC vegetation map. We have compared our model with CNN, DWT, and standard LU/LC classification and prediction techniques for the area of Javadi Hills. Our model outperforms the other standard classification and prediction algorithms in terms of accuracy and computational efficiency. We have executed the standard LU/LC algorithms (DWT [22], CNN [27], SVM [1], MLC [2], and RFC [25]) and provided a comparative analysis of the Vision Transformer model for our study area of Javadi Hills in Table 11. We have also presented the comparative accuracy of the classification model in Figure 22. We have also shown the comparative analysis of our prediction model with the hybrid machine-learning models [7] for the area of Javadi Hills in Table 12. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**LU/LC Class**} & \multicolumn{6}{c}{**Area (\%)**} \\ \cline{2-7}  & \multicolumn{6}{c}{**Year**} \\ \cline{2-7}  & **2012–2015** & **2015–2018** & **2018–2021** & **2021–2024** & **2024–2027** \\ \hline High Vegetation & \(-3.01\) & \(1.24\) & \(-1.55\) & \(-1.74\) & \(-0.96\) \\ Less Vegetation & \(6.76\) & \(-2.53\) & \(3.27\) & \(3.51\) & \(1.83\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Percentage of LU/LC change for the area of Javadi Hills during 2012–2027. Figure 21: LU/LC change analysis of the Javadi Hills, India (2012–2027). Our model outperforms the hybrid machine-learning models [7] and provides good prediction accuracy. We have validated the use of the LST map with other spatial maps that include a slope, aspect, and distances from the road map [7] for our prediction model. From the thorough analysis, we infer that the use of the LST map has a high impact on the LU/LC environment, and it has been considered an important spatial feature for the prediction of the LU/LC vegetation map. We have shown a few comparisons of the validation results of the LU/LC prediction methods by using LST, slope, aspect, and distance from the road map for the area of Javadi Hills in Table 13. \begin{table}
\begin{tabular}{c c} \hline \hline
**Study Area** & **Algorithm** & **Prediction Accuracy (\%)** \\ \hline \multirow{3}{*}{Javadi Hills, India} & Vision Transformer–based \\  & Bi-LSTM Model (ours) \\ \cline{2-3}  & RFC-based MC–ANN–CA \\  & Model [7] \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparative analysis of LU/LC prediction models for the area of Javadi Hills, India. Figure 22: Performance analysis of LU/LC classification model—Javadi Hills, India. We also show a few comparative analyses of overall prediction models for a few different study areas in Table 14. We observed that there is a performance variation in the prediction results for each study area around the world. This variation of the LU/LC classification and prediction results was due to the selection of study area, satellite data, environmental data, and its LU/LC classes.