Transfer learning for advancing natural hazard mitigation in civil engineering: a scoping review and future directions

Natural hazards cause severe damage to the built environment and community in general. In recent years, Machine learning (ML) has become a powerful tool in natural hazards engineering, offering accurate solutions for predicting, assessing, and managing natural hazard risks. Transfer learning (TL), a sub-domain of ML, has gained attention for its ability to transfer knowledge from relevant tasks to improve the model performance with limited data. There is an increasing interest in utilizing TL techniques for mitigating natural hazard risks. This study is designed to synthesize the literature at the intersection of transfer learning and natural hazards and identify the gaps in the literature for future direction. Specifically, a scoping literature review methodology is conducted to understand how the transfer learning techniques are applied in natural hazards engineering, emphasizing the impact to the civil engineering domain. From 904 records found on the Web of Science database, 122 studies were included based on inclusion criteria. The findings categorize the reviewed studies by natural hazard type, algorithm, data type, and civil engineering discipline, revealing key trends such as the dominance of parameter-based TL algorithms and the extensive use of image data. This review highlights the significance of TL in enhancing the resilience of infrastructure against natural hazards, providing a foundation for future research and development in this crucial area.