Mitigating domain shift problems in data-driven risk assessment models

This paper presents a domain adaptation algorithm that combines adversarial feature alignment and cycle-consistency restoration to address the domain shift problem in disaster risk assessment. By using adversarial networks, the model adapts features at the feature level, effectively leveraging unlabelled data, reducing the cost of data labelling, and minimizing the feature distribution differences between the source and target domains. Additionally, the introduction of cycle-consistency verification ensures the accuracy and consistency of feature transformation. The experimental results demonstrate that this algorithm performs exceptionally well in multiple real-world disaster risk assessment scenarios, significantly improving the accuracy and reliability of risk assessments compared with existing domain adaptation techniques. The key contributions of this research are as follows: (1) Utilizing adversarial learning to enable unsupervised domain adaptation, significantly reducing the need for labelled data and improving model adaptability in new environments; (2) introducing a training consistency-based adversarial learning method to preserve key information during domain adaptation, improving generalization in new domains; and (3) effectively addressing domain shift, enhancing model adaptability, and providing data-driven support for downstream decision-making, reducing disaster risk and resource waste. This approach not only advances disaster risk assessment but also promotes the broader application of unsupervised domain adaptation in various fields requiring fast and effective adaptation.