## 5 Results
### Analysis of a Robust BBN Model  
#### 5.2.1 Probabilistic Reasoning  
The developed robust BBN may be employed to perform probability reasoning, which includes calculations of posterior probability-sequential inference (from causes to results) and causal inference-reverse inference (from results to causes). (I) Liquefaction Potential Prediction  
Liquefaction potential prediction is shown in Figure 5 on the basis of the supposition that peak ground acceleration is \"medium\", groundwater table depth is \"shallow\", and soil behavior type index grade is \"silty sand or sand with silt\". On fixing these parameters as 100% in Netica, the state of the evidence variables is identified. Subsequently, the probabilities of a robust BBN model are updated; and the probability change of \"liquefaction potential\" and the remaining variable nodes are noted. In this instance, the \"yes\" state probability in \"liquefaction potential\" is identified to scale up from 58.2% to 63.6%. This indicates that when peak ground acceleration is \"medium\", groundwater table depth is \"shallow\", and soil behavior type index grade is \"silty sand or sand with silt\", the liquefaction potential probability of the \"yes\" state will notably increase (see Figure 5). As shown in Figure 6, in addition to \"medium\" peak ground acceleration, \"shallow\" groundwater table depth, and \"silty sand or sand with silt\" soil behavior type index, it was assumed that the depth of soil deposit status is \"shallow\" and the status of \"medium\" in \"equivalent clean sand penetration resistance\" is 100%. Thereafter, automatically updating the probabilities of robust BBN, the liquefaction potential probability of \"yes\" is found to further increase from 63.6% to 79.6%, which means that the liquefaction potential probability of \"yes\" status is higher with respect to the initial probability of 58.2%. This suggests that \"peak ground acceleration\", \"groundwater table\", \"soil behavior type index\", \"equivalent clean sand penetration resistance\", and \"depth of soil deposit\" will affect the liquefaction \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**OA**} & \multirow{2}{*}{**AUC**} & \multirow{2}{*}{**MCC**} & \multicolumn{4}{c}{**Liquefaction**} & \multicolumn{4}{c}{**Non-Liquefaction**} \\ \cline{5-10}  & & & & **Recall** & **Precision** & **F-Measure** & **Recall** & **Precision** & **F-Measure** \\ \hline BBN & **0.7600** & **0.7698** & **0.4910** & 0.7500 & **0.9000** & **0.8182** & **0.7857** & **0.5500** & **0.6471** \\ C4.5 DT & 0.7400 & 0.7450 & 0.3690 & **0.8060** & 0.8290 & 0.8170 & 0.5710 & 0.5330 & 0.5520 \\ CPT-YD & 0.7400 & 0.7103 & 0.3987 & 0.7778 & 0.8485 & 0.8116 & 0.6429 & 0.5294 & 0.5806 \\ CPT-RA & 0.7000 & 0.6607 & 0.3047 & 0.7500 & 0.8182 & 0.7826 & 0.5714 & 0.4706 & 0.5161 \\ \hline \hline \end{tabular} Note: OA = overall accuracy, AUC = area under curve, MCC= Matthews correlation coefficient. Bold numbers indicate the highest value in each confusion matrix category.  
\end{table} 
Table 6: Predictive performance evaluation of the testing dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**OA**} & \multirow{2}{*}{**AUC**} & \multirow{2}{*}{**MCC**} & \multicolumn{4}{c}{**Liquefaction**} & \multicolumn{4}{c}{**Non-Liquefaction**} \\ \cline{5-10}  & & & & **Recall** & **Precision** & **F-Measure** & **Recall** & **Precision** & **F-Measure** \\ \hline BBN & **0.9104** & **0.9168** & **0.7746** & **0.9583** & 0.9200 & **0.9388** & 0.7895 & **0.8824** & **0.8333** \\ C4.5 DT & 0.9055 & 0.9111 & 0.7640 & 0.9440 & **0.9250** & 0.9350 & **0.8070** & 0.8520 & 0.8290 \\ \hline \hline \end{tabular} Note: OA = overall accuracy, AUC = area under curve, MCC= Matthews correlation coefficient. Bold numbers indicate the highest value in each confusion matrix category.  
\end{table} 
Table 5: Performance evaluation of the training dataset. potential probability of the \"yes\" state to varying extents.