# An Evolutionary Neural Network Approach for Slopes Stability Assessment
## 2 Data Characterization  
As was already indicated, this work combines ANNs and GA with the goal of creating novel models to identify their stability, from this point referred to as EHC [29], of rock and soil cuttings as well as soil embankments. The EHC system is composed of four levels, \"A\", \"B\", \"C\", and \"D\", where the probability of failures increases from class \"A\" to \"D\". Three distinct databases were compiled for training and testing purposes, each containing information gathered during routine inspections and supplemented with geometric, geological, and geographic data for each slope. All three databases were compiled by NetworkRail employees and are related to the UK railway network. On the basis of their experience, NetworkRail engineers attributed a class of the EHC system to each slope, which was taken as a stand-in for the slope's actual stability conditions for the year 2015. Figure 2 represents the distribution of EHC levels. Its analysis reveals a significant number of available records for all three types of slopes, namely 5945 and 10,928 records for rock and soil cuttings, respectively, and 25,673 records for embankment slopes. We also noted a high asymmetric distribution (imbalanced data) of the records for each EHC class. For example, taking as reference the database for embankments, more than 63% of the data are classified as class \"A\", and only 2.5% belongs to class \"D\". Although this type of asymmetric distribution, where the majority of the slopes have a low failure probability (class \"A\"), is typical and desirable from the perspective of slope network management and slope safety, it can pose a significant learning challenge for DM models. In fact, when dealing with imbalanced classification tasks in which at least one target class label has fewer training samples than other target class labels, the straightforward application of a DM training algorithm frequently results in data-driven models with improved prediction accuracy for the majority classes and decreased classification accuracy for the minority classes. Thus, methods such as oversampling and the synthetic minority oversampling technique (SMOTE), which modify the data to be used for training purposes with the aim of balancing the target class, are frequently adopted to  
Figure 2: The data distribution of rock and soil cuttings from slopes and embankments by EHC class. handle datasets where the classes are not equally distributed (imbalanced). In particular, oversampling is a straightforward strategy that balances the final training set by adding random samples (with repetition) of the classes with fewer training data. SMOTE is a more advanced method that generates \"new data\" by establishing a neighborhood by examining the nearest neighbors and then sampling from that neighborhood. It assumes that the proximity of the original data makes it comparable. By creating synthetic instances, SMOTE helps to increase the representation of the minority class, making the dataset more balanced. This can lead to improved model performance, especially when the class imbalance is significant. It is important to note that SMOTE should be applied only to the training set and not the entire dataset. Also, it is recommended to combine SMOTE with other techniques, such as undersampling the majority class or using more advanced algorithms, such as the SMOTE-ENN (SMOTE with edited nearest neighbors) method, to further enhance the effectiveness of the resampling process. Under the SMOTE approach, regarding the nearest neighbours' \(k\) values, different values were tested before settling on \(k=3\), which achieved the highest performance. More than 50 variables were taken into consideration as model features, including information typically gathered during routine inspections, as well as data from geometric, geographic, and geological sources. To be precise, 65, 51, and 53 variables were considered, respectively, in the study of rock and soil cuttings and embankment slopes.