## 4 Results and Discussion
### Urban LULC Prediction
#### 4.3.4 LULC Model Application  
* Comparative study on the LULC prediction from the conventional MC and demographic-economics data to estimate the transition probability According to the previous section, the accuracy assessment confirms that the MLP-MC method yielded a strong level of perfect agreement between the simulated maps and reference data. The model application in this study adopted the transition rates calculated by MC in CA-MC and MLP-MC methods to simulate the LULC in 2014. The transition probabilities calculated from the population and economics via the extrapolation and SD were also utilized in the MLP as a platform to simulate in 2014. The LULC map in 2011 was used as a base map for the future simulation. The results from all schemes are illustrated in Figure 7. All values of the overall accuracy and Kappa coefficients illustrated and certify the performance of all schemes that were stratified to simulate the future LULC (Table 10). The transition rate calculated using the SD displayed the highest accuracy when compared with others. The SD scheme projected the urban area slightly lower than the existing LULC from the Landsat image; however, it showed a strong performance in the simulation (Table 11). From the extrapolation scheme, its result yielded an overestimation in the urban class and underestimation in agriculture class similar to the results from the conventional MC scheme when performed in CA and MLP environments. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Years**} \\ \cline{2-9}
**Types** & \multicolumn{2}{c}{**1998**} & \multicolumn{2}{c}{**2003**} & \multicolumn{2}{c}{**2008**} & \multicolumn{2}{c}{**2011**} \\ \cline{2-9}  & **CA-MC** & **MLP-MC** & **CA-MC** & **MLP-MC** & **CA-MC** & **MLP-MC** & **CA-MC** & **MLP-MC** \\ \hline Overall accuracy & 92.3728 & 92.1738 & 88.4218 & 90.5811 & 89.4320 & 89.4969 & 85.5354 & 85.5690 \\ K standard & 0.9097 & 0.9108 & 0.8748 & 0.8979 & 0.8913 & 0.8924 & 0.8570 & 0.8571 \\ Kno & 0.9374 & 0.9383 & 0.9088 & 0.9257 & 0.9168 & 0.9171 & 0.8859 & 0.8863 \\ KlocationStata & 0.9148 & 0.9146 & 0.9054 & 0.9298 & 0.9145 & 0.9072 & 0.9359 & 0.9356 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy assessment of simulated LULC maps from the MC in CA and MLP environments. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Years**} \\ \cline{2-9}
**LULC Classes** & \multicolumn{2}{c}{**1998**} & \multicolumn{2}{c}{**2003**} & \multicolumn{2}{c}{**2008**} & \multicolumn{2}{c}{**2011**} \\ \cline{2-9}  & **CA-MC** & **MLP-MC** & **CA-MC** & **MLP-MC** & **CA-MC** & **MLP-MC** & **CA-MC** & **  
* Simulation of the future urban LULC and urban expansion in 2035. Because of the strong performance of the demographic-economics data, the transition probabilities from the statistic extrapolation and SD schemes were applied to simulate the LULC and urban area. Both transition probabilities were calculated from the estimated population and annual growth rate of 2035. The simulation, in this part, was based on the transition potential map created by the MLP of the LCM model. Figure 8 illustrates the simulated results from both methods. It can visualize evidently that, by using the SD scheme, the predicted urban area is 3750.77 km\({}^{2}\) or 48.75 percent of the whole area of the BMR. Agricultural land is almost equal to the urban class. The lands for water, forest, and miscellaneous classes occupy the smallest amounts of land, sequentially (Table 12). In terms of the simulation from the extrapolation, all of the LULC have a similar trend with the SD scheme. However, the proportion of the LULC is different, especially in the urban class, which occupies around 64 percent of land.