## 4 Results and Discussion
### Urban LULC Prediction
#### 4.3.3 LULC Prediction and Validation  
* LULC Prediction from CA-MC and MLP-MC  
This study considered two different schemes for simulation based on the conventional model to explore the urban LULC and urban expansion in the BMR. The simulated LULC maps in the four time periods 1998, 2003, 2008, and 2011 were generated, as shown in Figure 6 and Table 8. The validation procedure was investigated to confirm the reliability and efficiency of the CA-MC and MLP-MC for the LULC prediction. The overall accuracy and several kinds of Kappa coefficients were applied to validate the simulated maps with the LULC maps generated by the maximum likelihood classification. After the superimposing, all simulated maps yielded overall accuracies greater than 80 percent. Furthermore, Kappa coefficients such as Kstandard, Kno, and KlocationStrata were also calculated to assure the accurate result of the simulation, all of which had values higher than 0.8 (Table 8). However, the accuracy assessment from the simulated LULC maps proved that the MLP-MC method contributed higher accuracy values than CA-MC. Since the MLP-MC deliberated the influence in physical, demographic and economic factors on the LULC conversion, the simulated maps from this method showed a higher accuracy when compared with the CA-MC results (Table 9 and Figure 6). CA-MC does not, in contrast, consider the change spatially. This method is not always appropriate to allocate future LULC in suitable locations [14; 55].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Classes**} & **Agriculture** & **Water** & **Urban** & **Forest** & **Miscellaneous** \\ \hline \multirow{5}{*}{Extrapolation} & Agriculture & 0.8426 & 0.0099 & 0.1393 & 0.0030 & 0.0052 \\  & Water & 0.1541 & 0.7854 & 0.0481 & 0.0125 & 0.0000 \\  & Urban & 0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000 \\  & Forest & 0.2861 & 0.0234 & 0.0326 & 0.6255 & 0.0324 \\  & Miscellaneous & 0.0809 & 0.0030 & 0.7959 & 0.0056 & 0.1146 \\ \hline \multirow{5}{*}{SD} & Agriculture & 0.8995 & 0.0055 & 0.0896 & 0.0029 & 0.0026 \\  & Water & 0.2016 & 0.7976 & 0.0007 & 0.0002 & 0.0000 \\ \cline{1-1}  & Urban & 0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000 \\ \cline{1-1}  & Forest & 0.4011 & 0.0001 & 0.5986 & 0.0002 & 0.0000 \\ \cline{1-1}  & Miscellaneous & 0.0079 & 0.0001 & 0.0051 & 0.0002 & 0.9868 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Transition probability rate in each LULC class calculated by extrapolation and system dynamic model for 2014.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Classes**} & **Agriculture** & **Water** & **Urban** & **Forest** & **Miscellaneous** \\ \hline \multirow{5}{*}{Extrapolation} & Agriculture & 0.5941 & 0.0000 & 0.3860 & 0.0022 & 0.0177 \\  & Water & 0.1717 & 0.7269 & 0.0728 & 0.0286 & 0.0000 \\  & Urban & 0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000 \\  & Forest & 0.1458 & 0.0862 & 0.6602 & 0.1000 & 0.0078 \\  & Miscellaneous & 0.7859 & 0.0136 & 0.0000 & 0.0009 & 0.1996 \\ \hline \multirow{5}{*}{SD} & Agriculture & 0.5365 & 0.0253 & 0.4130 & 0.0133 & 0.0118 \\  & Water & 0.1535 & 0.7644 & 0.0647 & 0.0158 & 0.0017 \\ \cline{1-1}  & Urban & 0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000 \\ \cline{1-1}  & Forest & 0.2276 & 0.0012 & 0.7668 & 0.0042 & 0.0002 \\ \cline{1-1}  & Miscellaneous & 0.1366 & 0.0018 & 0.7703 & 0.0034 & 0.0879 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Transition probability rate in each LULC class calculated by extrapolation and system dynamic model for 2035.  
Figure 6: Comparison of LULC prediction and urban extend between Cellular Automata-Markov Chain (CA-MC) and Multi-Layer Perceptron-Markov Chain (MLP-MC): (**a**) CA-MC 1998; (**b**) MLP-MC 1998; (**c**) CA-MC 2003; (**d**) MLP-MC 2003; (**e**) CA-MC 2008; (**f**) MLP-MC 2008; (**g**) CA-MC 2011; and (**h**) MLP-MC 2011.