# An Evolutionary Neural Network Approach for Slopes Stability Assessment
## 4 Results and Discussion
### Model Performance  
A single model was chosen from all Pareto optimal solutions for a each slope type on the basis of the recall, precision, and F\({}_{1-\text{score}}\) performance metrics across all EHC classes. As summarized in Section 3.5, all Pareto optimal solutions were retrained using the SMOTE and oversampling techniques, as well as without resampling (normal). The efficacy of these Pareto optimal solutions for each slope type is presented and discussed in the following sections. Using the recall, precision, and F\({}_{1-\text{score}}\) metrics as benchmarks, Figure 7 compares ANNs models from the first ANN models (from now referred to as the \"old\" models [30; 31])  
Figure 6: Number of times that each input feature is selected as model attribute over all Pareto optimal solutions. and the best ANN models selected among all Pareto optimal solutions (referred to from now on as the \"updated\" models). This comparison was made by calculating the difference between the metrics of the \"updated\" and \"old\" models. Hence, positive values correspond to the superior performance of the \"updated\" models. Despite the fact that the overall difference between the \"updated\" and \"old\" models is not statistically significant, the \"updated\" models have several key advantages over the \"old\" ones. On the one hand, using the recall metric as a benchmark, the \"Updated\" models, particularly those following a SMOTE approach, demonstrate a significant improvement in the prediction of classes \"C\" and \"D\". On the other hand, this \"updated\" model utilizes a significantly reduced number of features. On average, less than 30 variables were considered instead of the more than 50 considered in the \"Old\" models. These two aspects represent a significant accomplishment from a practical standpoint. First, less information is required to accomplish the same overall performance, which represents significant time and cost savings. Second, a better performance on classes \"C\" and \"D\" prediction was achieved with almost no performance penalization for the remaining classes. This trade-off between the models' responses for the major and minority classes is a consequence of the highly asymmetric distribution of the database. However, it should be noted that the \"updated\" models achieved a better balance by increasing their performance for the minority classes, for which the probability of failure is higher, without significantly compromising their responses for the major classes. When analyzing the effect of the training sampling approaches (oversampling and SMOTE), some effectiveness can be observed for classes \"C\" and \"D\", for which the probability of failure is higher (as these are minority classes), particularly for rock cuttings. Concerning classes \"A\" and \"B\", the implementation of a sampling approach does not produce the same effectiveness. When comparing SMOTE and oversampling, the effectiveness of the first method seems to be slightly superior. These results are in line with those observed during the first iteration, as reported in [30; 31]. Figure 7: Comparison of the best ANN models (“updated” vs. “old”) based on recall, precision and F\({}_{1-\text{score}}\) according to a nominal classification strategy in slope stability prediction of rock and soil cuttings, slopes, and embankments. Figure 8 depicts the relationship between the observed and predicted EHC classes based on the \"updated\" SMOTE-based ANN model. Its superior performance on classes \"A\" and \"B\" is evident from its analysis. Particularly for soil cuttings and embankments, we can also observe a very interesting response for class \"D\" (more than 60% of the slopes belonging to class \"D\" were correctly identified). Furthermore, one can observe that when a slope's class is not correctly identified, it is categorized as belonging to the closest class. For instance, a slope of class \"D\" that is not predicted as \"D\" is typically classified as \"C\".