## 3 Materials and Methods
### Model Assessment Criteria  
This section explains the measures of fit to evaluate the performance of ANNs. These criteria are applied in the model assessment for classification problems with binary or multi-level categorical output variables. The overall accuracy rate is a measure of how successful the model is in correctly grouping the predicted categorical response as the actual one; it is presented as the numerical difference of 1 and the misclassification rate. The misclassification rate is the rate at which the categorical response group with the highest fitted probability is not the actual group. The confusion rate is a measure that shows the percentage of correct classification of a categorical response, and is expressed in the form of a contingency table, where the diagonal values show the correct classification frequency of a multi-level categorical response. Area under the curve (AUC) is an indicator of the predictive model goodness of fit, and is gained from the receiving operating chart (ROC). Past research has proven that the ROC curve is a standard and useful tool to determine the quality of deterministic and probabilistic models [50]. The ROC curve provides a comprehensive and visually attractive way to summarize the accuracy of predictions [51]. It is widely applicable, regardless of the source of predictions. In the ROC curve, on the X axis, the \"sensitivity\" of the model is plotted, against \"100-specificity\" on the Y axis [52]. The area under the ROC curve (AUC) shows the ability of a model to predict the correct occurrence or non-occurrence of landslide events. As the AUC values increase, the predictive capability of the model  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Independent Variable** & **Chi-Square (\(\chi^{2}\))** & _p-Value_ \\ \hline Type of injury & 316.077 & \textless{}0.0001 * \\ Nature of injury & 7544.33 & \textless{}0.0001 * \\ Class Description & 145.63 & \textless{}0.0001 * \\ Age & 253.07 & \textless{}0.0001 * \\ Experience & 60.78 & \textless{}0.0001 * \\ Gender & 7.97 & 0.1578 \\ \hline \hline \end{tabular}  
* _p-value \(<\alpha=0.05\)_ statistically significant variable.  
\end{table}
Table 1: Variable importance using chi-square test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Structure Criteria** & **MLP** & **RBF** \\ \hline \multirow{7}{*}{Input Layer} & \multicolumn{2}{c}{Injury Nature} & Injury Nature \\  & \multicolumn{2}{c}{Class Description} & Class Description \\  & \multicolumn{2}{c}{Factors} & Injury Type & Injury Type \\  & \multicolumn{2}{c}{Experience} & Experience & Experience \\  & \multicolumn{2}{c}{Age} & Age \\  & \multicolumn{2}{c}{Number of Units} & 4688 & 4679 \\  & \multicolumn{2}{c}{Number of Hidden Layers} & 2 & 1 \\ Hidden Layer(s) & \multicolumn{2}{c}{Number of Units in Hidden Layer} & 12 & 10 * \\  & \multicolumn{2}{c}{Activation Function} & Hyperbolic tangent & Softmax \\  & \multicolumn{2}{c}{Dependent Variables} & Cause Group & Cause Group \\ Output Layer & \multicolumn{2}{c}{Number of Units} & 6 & 6 \\  & \multicolumn{2}{c}{Activation Function} & Softmax & Identity \\  & \multicolumn{2}{c}{Error Function} & Cross-entropy & Sum of Squares \\ \hline \hline \end{tabular}  
* The ”best” number of hidden units is the one that yields the smallest error in the testing data.  
\end{table}
Table 2: MLP vs RBF Network Structure.  
is better. According to [PERSON] and [PERSON] [53], the quantitative-qualitative relation between the AUC and prediction accuracy can be classified as follows: 0.5-0.6 (poor), 0.6-0.7 (average), 0.7-0.8 (good), 0.8-0.9 (very good), and 0.9-1(excellent).