# An Evolutionary Neural Network Approach for Slopes Stability Assessment
## 1 Motivation and Background
We particularly note that the two initial slope stability prediction studies [30; 31] assumed a fixed set of selected input variables that could easily be collected during routine inspection activities. The main goal was to compare two learning models (ANN and SVM) for both regression and classification tasks. The preliminary studies [30; 31] provided some useful insights that helped to design the approach proposed in this work. Firstly, the best predictive results in both works were produced by the ANN model when performing a nominal classification. Secondly, while interesting predictive results were achieved by the ANN model, the performance was far from ideal. In particular, a high error was obtained for the infrequent EHC (earthwork hazard category) classes (which are related to more hazardous conditions). Thirdly, although the implementation of resampling techniques to handle the imbalanced data issue (which arises as the majority of slopes are in perfect condition) has not always been effective; in some instances, it aided the algorithms in better learning the problem. Such an asymmetric distribution of the data represents a key challenge of the work since it affects the model's response over all four EHC classes. Fourthly, it was observed that the identification of slope stability is governed by a specific group of characteristics. Figure 1 compares the performance of ANN models when identifying the slope stability of rock and soil cuttings as well as soil embankments based on recall, precision, and F\({}_{1-\text{score}}\) metrics. More information about the achieved performance can be found in the published works [30; 31]. As demonstrated, and taking recall measure as a benchmark, an exceptional prediction ability was observed for class \"A\". Class \"B\" also exhibited strong accuracy. Nevertheless, for classes \"C\" and \"D\", which had a higher probability of failure, the attained performance fell short of expectations, specifically for rock cuttings. Following the same strategy adopted during the first attempt (i.e., the use of information that can be readily obtained through routine visual inspections [30,31]), the researchers conducted additional tests. In this research, we propose a novel method to improve the performance of models identifying the stability of rock and soil cuttings, as well as embankments. Thus, this paper combines two artificial intelligence (AI) techniques: genetic algorithms (GA), which are popular metaheuristics methods for optimization, and ANNs, which have non-linear learning capabilities. This combination, known as an evolutionary neural network (ENN), allows us to gain interesting capabilities from both AI techniques. In particular, GA is used to perform a search of the best set of input features, which represents one of the major challenges of this work, thus performing automatic feature selection. When compared with other GA input feature optimization works (e.g., [35]), the novelty of our GA approach is that we adopt Pareto curve optimization, allowing us to simultaneously optimize performance measures for all four EHC classes (and not just a single overall performance measure). We note that Pareto curve optimization methods need to keep track of a population of diverse solutions, and GAs are a natural and popular approach for such multi-objective optimization tasks [36]. As for the ANN, it uses a selected set of features as inputs and performs a non-linear mapping with the target output, allowing us to automatically obtain data-driven multi-class classifiers. As underlined above, it is expected that by using a reduced number of features to feed the ANNs, their predictive performance will improve. Finally, a detailed sensitivity analysis was applied to the best ENN models for each one of the three types of slopes, allowing us to identify the key variables in the identification of slope stability.