An Integration of Deep Learning and Transfer Learning for Earthquake-Risk Assessment in the Eurasian Region

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-08, Vol.15 (15), p.3759
• Main Authors: Jena, Ratiranjan, Shanableh, Abdallah, Al-Ruzouq, Rami, Pradhan, Biswajeet, Gibril, Mohamed Barakat A., Ghorbanzadeh, Omid, Atzberger, Clement, Khalil, Mohamad Ali, Mittal, Himanshu, Ghamisi, Pedram
• Format: Article
• Language: English
• Subjects: Artificial intelligence; artificial intelligence (AI); China; Construction; Deep learning; earthquake risk; Earthquakes; Environmental risk; Eurasia; Evaluation; Fault lines; Fractals; Geographic information systems; Hazard assessment; India; Iran; Model accuracy; Nuclear energy; Nuclear power plants; Remote sensing; Risk assessment; Seismic activity; Seismic hazard; Stability analysis; Transfer learning; Urban areas; vulnerability mapping
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15153759

The problem of estimating earthquake risk is one of the primary themes for researchers and investigators in the field of geosciences. The combined assessment of spatial probability and the determination of earthquake risk at large scales is challenging. To the best of the authors’ knowledge, there no updated earthquake-hazard-and-risk assessments for the Eurasia region have been published since 1999. Considering that Eurasia is characterized by a seismically active Alpine–Himalayan fault zone and the Pacific Ring of Fire, which are frequently affected by devastating events, a continental-scale risk assessment for Eurasia is necessary to check the global applicability of developed methods and to update the earthquake-hazard, -vulnerability, and -risk maps. The current study proposes an integrated deep-transfer-learning approach called the gated recurrent unit–simple recurrent unit (GRU–SRU) to estimate earthquake risk in Eurasia. In this regard, the GRU model estimates the spatial probability, while the SRU model evaluates the vulnerability. To this end, spatial probability assessment (SPA), and earthquake-vulnerability assessment (EVA) results were integrated to generate risk A, while the earthquake-hazard assessment (EHA) and EVA were considered to generate risk B. This research concludes that in the case of earthquake-risk assessment (ERA), the results obtained for Risk B were better than those for risk A. Using this approach, we also evaluated the stability of the factors and interpreted the interaction values to form a spatial prediction. The accuracy of our proposed integrated approach was examined by means of a comparison between the obtained deep learning (DL)-based results and the maps generated by the Global Earthquake Model (GEM). The accuracy of the SPA was 93.17%, while that of the EVA was 89.33%.