## 4 Proposed LU/LC Prediction Using Vision Transformer-Based Bi-LSTM Model
### Validation of Vision Transformer-Based Bi-LSTM Model

The Google Earth images with the LU/LC classified images were evaluated for the examination of accuracy assessment. By using the time-series images of the Google Earth Engine, the accuracy assessment was calculated for the LU/LC classified image of Javadi Hills. All the pixel values of the LU/LC classified image were validated with the Google \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Input Image Format & Raster \\ Number of Training Samples & 51,200 \\ Activation Function & _tanh_, Softmax \\ Dropout & 0.1, 0.25 \\ Learning Rate & 0.001 \\ Optimizer & Adam \\ Loss Function & Categorical Cross Entropy \\ Hidden layers & 20 \\ Number of epochs & 100 \\ Batch Size & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hyperparameters for the Bi-LSTM model. Figure 20: Training LU/LC–LST feature map for Bi-LSTM prediction model—Javadi Hills, India. Earth images. A total of 1008 random training samples were loaded, and the confusion matrix was obtained during the process of accuracy assessment. Table 6 represents the confusion matrix for the years 2012 and 2015. The results of the accuracy assessment for the year 2012 are 0.9891, and for 2015, it is 0.9861. Table 7 represents the LU/LC accuracy assessment for the years 2012 and 2015. The LU/LC prediction was performed, and the results were analyzed and processed. The total number of pixel values was sliced into training and validation sets in an 8:2 proportion. The accuracy values of the prediction method look good for the LU/LC map of 2018 and 2021. The result of the validation accuracy for the year 2018 is 0.9865, and for 2021, it is 0.9811. The results were also cross-verified with the time-series Google Earth Engine image of Javadi Hills for the years 2018 and 2021 for acquiring the testing accuracy of our model. The results of the testing accuracy for our model also provide good results for 2018 and 2021. The results of the testing accuracy for the year 2018 is 0.9696, and for 2021, it is 0.9673. The results of the testing and validation accuracy of the predicted map are presented in Table 8. The validation accuracy refers to the results of the non-trained datasets of the model. The testing accuracy refers to the results of the complete model. We used the inputs of the LU/LC map of 2012 and 2015, along with the predicted LU/LC map of 2018 and 2021 for predicting the LU/LC map for the years 2024 and 2027. The short-term prediction was performed till the year 2027 for our study area. As the Google Earth Engine provides the time-series image till the current date, the validation and testing accuracy for the predicted LU/LC map of 2024 and 2027 was not calculated. With the results of the good validation accuracy for all the LU/LC predicted maps of Javadi Hills, our prediction model provides a lower misclassification rate. \[\textit{Average Model Accuracy}=\left(\frac{A_{Y1}+A_{Y2}+\ldots+A_{Yn}}{ \text{T}}\right)\;*\;100 \tag{27}\]

where \(A_{Y}\) represents the accuracy value of years \(\{1\ldots.n\}\), and T represents the total number of years. The importance of providing the performance of the model depends on the average classification and prediction results. The average classification and prediction accuracy for the time series LU/LC data have been calculated by using Equation (27). The accuracy results for the years 2012 (0.9891) and 2015 (0.9861) were used for providing the performance of the calculation model through the average model accuracy. The average classification accuracy that was obtained was 98.76% for the proposed Vision Transformer model. The validation and testing results of our prediction model for the year 2018 are 0.9865 and 0.9696, respectively. The validation and testing results of our prediction model for the year 2021 are 0.9811 and 0.9673, respectively. The average validation accuracy is