## 5 Results and Discussions
### Testing
The slopes were sorted based on their true _FS_s. A solid line was then used to represent the true _FS_ values of the slopes. There are five types of markers in this figure: four of them belong to the four LEMs implemented in this study, and one is for the predictions of the regression model. The results are demonstrated in two sections for plotting purposes. The main plot shows 200 images for _FS_ values up to 1.8, and the subplot at the upper left corner shows 26 slopes with higher _FS_s. The comparison indicates that all of the predicted _FS_s were quite close to the true _FS_s calculated as the average _FS_ of the four LEMs. Further analysis of this plot shows that the BSM underestimated the _FS_s for most slopes, while JCM and JSM tended to overestimate them. Despite this difference, the predictions of the regression model, true _FS_s, and the OMS results were similar to one another. This plot also shows that, for the majority of the slopes, the predictions of the deep learning models were within the highest and lowest _FS_s obtained with the four LEMs and were more accurate than all of them. Another important observation from this figure is the ability of the regression model to distinguish between images within the first and ninth categories. While there was only one class for each of these wide ranges in the classification model, the regression model could estimate the _FS_ values of these images instead of categorizing them as \"lower than 0.8\" (first category) or \"higher than 1.5\" (ninth category). Figure 9: The comparison between the LEM and the classification model. The other major criteria adopted in this comparison study were the computational demands and time-efficiency. Using LEMs to evaluate slope stability is a time-consuming process. This process is comprised of two main stages: manual construction of the model and using it to evaluate slope stability. By contrast, the ability of deep learning to quickly analyze large amounts of data is one of the main reasons for its popularity and widespread use. In this study, the amount of time needed for the LEMs to calculate the _FS_ values (the second stage) was compared with that of the two proposed deep learning models. It is noted that the computing times for the classification and regression models were almost the same, so the computing time of the regression model is not shown in the plot. Figure 11 summarizes a comparison of the computing times needed for calculating the _FS_ with the LEMs and that with the classification model. In this figure, each line with markers represents the deep learning model or a traditional LEM for calculating the _FS_. This figure shows that the deep learning model outperformed the traditional LEMs in terms of computation time. In addition, the amount of time needed for the LEMs increased approximately linearly as the number of cases increased. For example, the time needed to calculate the _FS_s of 200 slopes was almost twice the time needed for 100 slopes. However, deep learning methods behave differently. Every time a deep learning model is deployed, it first runs several background processes and then starts predicting _FS_ values. Therefore, as the number of images increased, this initial cost was distributed over more images. That is why the deep learning model outperformed the LEMs more and more as the number of images in the testing dataset increased. To emphasize this boost in performance, the elapsed time for the classification model (blue line) and JCM (green line) were also labeled. For example, it took 15.1 s for the classification model to predict the _FS_ for 200 slopes, while obtaining the _FS_ via JCM for the same number of images took 1315 s. This means that deep learning methods are over 18 times more efficient than JCM. Aside from that, when analyzing 200 cases, the improved computational efficiency of the deep learning methods would be more notable if the time needed for manual procedures prior to running the model (the first stage) were considered for traditional LEMs.