Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil

Empirical models based on machine learning methods have been used for landslide susceptibility mapping. The most accurate model is usually chosen to generate the final map. This paper demonstrates the importance of analyzing the spatial pattern of susceptibility maps, since models with similar performance can produce different output values. The relevance of terrain attributes and the sensitivity of models to input variables are also discussed. The applications of random forest (RF) and artificial neural network (ANN) models to the identification of landslide susceptible areas in the Fão River Basin, Southern Brazil, were evaluated and compared. The following have been included in the methodology: (1) the extraction of predictive attributes (e.g., slope, aspect, curvatures, valley depth) from a digital elevation model; (2) the organization of a landslide scar inventory; (3) the calibration and validation procedures of the models; (4) the analysis of model performance according to accuracy (area under the receiver operating characteristic curve) and parsimony (Akaike Information Criterion); (5) the reclassification of maps into susceptibility categories. All model configurations resulted in an accuracy above 0.9, demonstrating the ability of both techniques in landslide susceptibility mapping. The RF model stood out in this respect, recording the highest accuracy index among all tested configurations (0.949). The ANN model was more parsimonious, obtaining an accuracy of 0.925 with a much smaller number of internal connections. Thus, even with both having high and equivalent accuracy indexes, the models can establish different relationships between the input and the output susceptibility indexes, resulting in various possible landslide occurrence scenarios. These differences, together with the difficulty in defining which model presents more coherent results, reinforce the possibility of extracting spatial statistics, considering multiple configurations of models that combine accuracy and parsimony, in landslide susceptibility mapping.