## 6 Conclusions  
This paper introduces deep learning-based methods as a new category of methods to evaluate slope stability. To achieve this, a multiclass classification model and a regression model in deep learning were employed to develop two slope stability analysis methods in this category. The performance of these methods was assessed in different aspects. The new methods exhibited promising performance in terms of both accuracy and computational efficiency. For accuracy, the classification model showed an accuracy of 82% in training and 80.9% for testing in a deep learning perspective. Further analysis revealed that, if the predictions that were off by one category were also considered acceptable, the testing accuracy would get as high as 99.71%. This indicated that the loss of information caused by using categories to represent a continuous variable (_FS_) was the reason for the lower accuracy of the classification model. By contrast, the use of the regression model prevented this loss of information and increased the deep learning accuracy to 97.73%. Aside from high accuracy, the ability of CNNs to analyze raw image data obviates the need for the time-consuming and error-prone procedures of constructing conventional models, which are required for LEMs and SRMs. Using raw data directly also means that we can avoid the simplifying assumptions associated with conventional methods, which could jeopardize the accuracy of stability analysis. Another advantage of using CNNs is their efficiency; much shorter computing times, compared with conventional methods, are needed with the same computing resources. The results showed that, even without considering the time needed for manual construction of the LEMs, the proposed CNNs were 18 times faster than JCM when analyzing 200 cases. It is also noteworthy that this difference in the computing time would even increase as the size of the data increased.  
This study can pave the way toward adopting deep learning to analyze complex geosystem and geohazard problems and can be extended to other stability problems in engineering. It is also worthwhile to mention that the proposed models are based upon traditional LEMs and are limited by the constraints in their training data. Therefore, future studies should consider easing the constraints on simple geometries, using inhomogeneous soil properties, and incorporating pore pressure to make the study more practical. This concept can be further developed to use remote sensing and geographic information systems to analyze the stability of slopes in real time.  
Figure 11: Computing time for the classification model and the LEM code.  
**Author Contributions:** Conceptualization and methodology, Z.L. and B.A.; software and validation, B.A. and A.B.; writing--original draft preparation, B.A.; writing--B.A. and Z.L.; supervision and project administration, Z.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors would like to acknowledge the financial support from the United States National Science Foundation (NSF) via Award 1742656 from the Geotechnical Engineering and Materials Program (now part of CMMI ECI). This work also benefited from the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the National Science Foundation grant number ACI-1548562.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data will be available upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.