Data–driven classification of landslide types at a national scale by using Artificial Neural Networks

Abstract

Classification of landslide type is an essential step in risk management, although is often missing in large inventories. Here we propose a novel data-driven method that uses easily accessible morphometric and geospatial input parameters to classify landslides type at a national scale in Italy by means of a shallow Artificial Neural Network. We achieved an overall True Positive Rate of 0.76 for a five-class overall classification of over 275,000 landslides as (1) rockfall/toppling, (2) translational/rotational slide, (3) earth flow, (4) debris flow, and (5) complex landslide. In general, the model performance is very good in the entire national territory, with large areas reaching F-score higher than 0.9. The method can be applied to any polygonal inventory, as those produced by automatic mapping procedures from Earth Observation imagery, in order to automatically identify the types of landslides.

Keywords

• Data-driven classification
• Artificial Neural Network
• Machine Learning
• Landslide inventory
• Landslide type
• Geospatial modelling