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

Creative Commons License

Di Michele F., Stagnini E., Pera D., Rubino B., Aloisio R., Askan Gündoğan A., ...Daha Fazla

Natural Hazards, cilt.116, sa.3, ss.3521-3546, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 116 Sayı: 3
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s11069-023-05822-4
  • Dergi Adı: Natural Hazards
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Environment Index, Geobase, INSPEC, Metadex, PAIS International, Pollution Abstracts, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.3521-3546
  • Anahtar Kelimeler: Seismic damage prediction, Machine learning, L'Aquila 2009 earthquake, Ground motion, FEATURE-SELECTION, PREDICTION
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

© 2023, The Author(s).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.