Comparison of machine learning tools for damage classification: the case of L'Aquila 2009 earthquake

On April 6, 2009, a strong earthquake (6.1 Mw) struck the city of L'Aquila, which was severely damaged as well as many neighboring towns. After this event, a digital model of the region affected by the earthquake was built and a large amount of data was collected and made available. This allowed us to obtain a very detailed dataset that accurately describes a typical historic city in central Italy. Building on this work, we propose a study that employs machine learning (ML) tools to predict damage to buildings after the 2009 earthquake. The used dataset, in its original form, contains 21 features, in addition to the target variable which is the level of damage. We are able to differentiate between light, moderate and heavy damage with an accuracy of 59%, by using the Random Forest (RF) algorithm. The level of accuracy remains almost stable using only the 12 features selected by the Boruta algorithm. In both cases, the RF tool showed an excellent ability to distinguish between moderate-heavy and light damage: around the 3% of the buildings classified as seriously damaged were labeled by the algorithm as minor damage.

Automated summary

On April 6, 2009, an earthquake caused extensive damage to the city of L'Aquila and nearby towns. A digital model and a detailed dataset were created to study the damage. Machine learning tools, specifically the Random Forest algorithm, were used to predict the level of building damage. The algorithm achieved an accuracy of 59% in classifying light, moderate, and heavy damage.