## 5 Results and Discussions
### Testing  
As was mentioned before, the goal of this study was to use both trained models to predict the \(FS\)s of slopes based on their images. To provide an unbiased assessment of the prediction performance of the models, a comprehensive testing dataset that was independent of the training images was needed. In addition, the testing dataset should have included cases from all ranges of the \(FS\). Accordingly, more than 30,000 cases were generated using the computer code. Table 1 was then used to distribute these images into nine classes based on their \(FS\)s. One thousand slope images per category were randomly selected to form the testing dataset. The result was a dataset of 9000 slope images, for which the true \(FS\) and the range or class that it belonged to was known. The trained classification and regression models were then employed to predict the range and the value of the true \(FS\), respectively. It is important to note that the inputs in the testing phase of the study were the unlabeled slope image data. Therefore, the deep learning models did not get any information regarding the true \(FS\)s of the slopes in the testing data. In the following, the prediction accuracy and computational efficiency of the deep learning-based methods and LEMs are compared against one another. Figure 7 compares the predictions made with the trained models against the true \(FS\)s obtained with the computer code. The x-axes in both subplots represent the true \(FS\) values obtained with the computer code, and the y-axes are the \(FS\) values predicted with the deep learning models. In Figure 7a, both axes are divided into nine regions, corresponding to the number of classes in the classification model. In this subplot, each cell is color-coded based on the number of images that it represents. These numbers are also shown on each cell. The cells on the diagonal line indicate slopes in which the predicted and true \(FS\)s are identical. These cells are colored light green, which suggests a high number of images according to the colormap of this plot. The dark green cells represent images for which the distance between the predicted and true \(FS\) values was 0.1 (one category). The red cells represent \(FS\) predictions that were off by more than 0.1 (more than one category). The blank cells imply that there were no cases in these cells. Further analysis of Figure 7a shows that the testing  
Figure 6: Learning curve for the classification model. accuracy of the classification model was 80.9%, which was obtained by calculating the sum of the values on the diagonal line (7283) and dividing it by the total number of cases (9000). Additionally, the mean absolute error of prediction was 0.0195. The low gap between the training (82%) and testing accuracy (80.9%) indicates the absence of overfitting. In Figure 7b, each dot represents one image in the testing dataset. The prediction accuracy of the regression model can be interpreted as the distance between the dots in this plot and the diagonal line. Thus, the closer a dot is to the diagonal line (\(y=x\)), the more accurate its predicted \(FS\) value is. As can be seen in this plot, while in some cases the predicted \(FS\) was far from the true value, the number of such cases was small compared with the total number of cases (9000). The mean absolute error for the regression model was 0.0265. The accuracy of classification was 80.9%, which does not appear to be high from a deep learning perspective. However, the accuracy could be much higher from the perspective of slope stability analysis. The reason for this is that any incorrect classification is counted as \"bad\" in deep learning.