Parsimonious design of pattern recognition systems for slope stability analysis

State-of-the-art machine learning methods, such as (deep) neural networks and support vector classifiers, have been successfully applied to problems related to the prediction of natural hazard events. However, the effectiveness of those methods is, in general, obtained at the cost of a complex algorithmic architecture that requires fine-tuning of several parameters. Moreover, their current popularity as a hot topic in the literature makes researchers to ignore simpler and classical methods that might perform equally well and should be preferred according to the Occam's razor principle. In this paper we exemplify this case by showing that two particular problems of slope stability prediction -that were recently solved using complex approaches named bee colony optimized support vector classifier and metaheuristic-optimized least squares support vector classifier, respectively- can be successfully solved by much more simpler pattern recognition methods. We also emphasize on the importance of data visualization and incremental evaluation during the design cycle of a parsimonious pattern recognition system.