## 5 Results and Discussions
### Testing
However, in slope stability analysis, the effectiveness of the results can also be measured by the distance between the predicted classification, (i.e., the \(FS\)) and the real classification; that is, predictions that are not far from the real classification (i.e., an \(FS\) value that is 0.1 above or below the true \(FS\)) is not \"accurate\" in deep learning but can still be acceptable in slope stability analysis. A reinterpretation of the results in Figure 7a revealed that more than 98% of the incorrectly predicted cases in deep learning would be acceptable in slope stability analysis. The large number of cases that were one category off was potentially caused by the defined ranges. To gain more insights, we divided a continuous variable (\(FS\)) into nine categories to utilize a classification model. While this is a necessary step to prepare the dataset for the classification model, it may also lead to the loss of information. Once the labels are created, this loss of information can affect the classification model in the following ways. First, the model is unable to reflect the difference between the \(FS\) values of images that belong to the same category. Second, the model is incapable of distinguishing between images that have close \(FS\) values but belong to two nearby categories. We can reduce this loss of information by choosing smaller ranges, leading to a higher number of categories. The regression model here can be viewed as a classification model with an infinite number of categories or classes. The effect of this loss of information can be better quantified with statistical analysis. Figure 8 is presented to investigate the magnitude of errors in the predictions. In these semi-logarithmic plots, the \(x\)-axis denotes the error in  
Figure 7: Results of testing for (**a**) the classification model and (**b**) the regression model. the predictions of the models, and the \(y\)-axis displays the number of slope images with a particular error. As is shown, if the predictions that were off by one category were considered acceptable, the accuracy of the regression and classification models would be 99.69% and 99.71%, respectively. Additionally, in the deep learning perspective, the regression model outperformed the classification model and decreased the number of predictions that were off by one category from 1691 to 176. This increased the deep learning accuracy from 80.92% in the classification model to 97.73% in the regression model. This leap in performance confirms that the loss of information associated with grouping _FS_ values was responsible for a large portion of the errors in the classification model. To further evaluate the effect of this loss of information, 500 random slope images were selected from the testing data. In Figure 9, the predicted range of _FS_ values obtained by the classification model is compared with the true _FS_ values. In this figure, the vertical axis is the true _FS_ value of each point or image, and the horizontal axis is the predicted _FS_ value. It is important to note that the classification model predicts the range of _FS_s, and the horizontal placement of points within a single range is for plotting purposes. The points that are located within the green boxes represent cases in which the predicted _FS_ value was correct. By contrast, the points in red boxes belong to incorrectly predicted ranges. This figure shows that for the majority of the incorrectly predicted cases, the true _FS_ value was quite close to the boundaries of the _FS_ ranges defined for the classification model. This confirms that the loss of information made it difficult for the classification model to predict the range of _FS_ values when the true _FS_ was close to the cell boundaries. Figure 8: Number of images versus error in _FS_ predictions for (**a**) the classification model and (**b**) the regression model. Figure 10 plots the _FS_ values obtained by different analysis methods for a subset of the testing data. This figure contains 226 randomly selected slope images.