A Case-Based Reasoning Framework Augmented with Causal Graph Bayesian Networks for Multi-Hazard Assessment of Earthquake Impacts

Earthquakes often lead to significant secondary hazards such as landslides, liquefaction, and aftershocks, which in turn cause great damage to buildings and seriously jeopardize socio-ecological welfare. The prevailing models for post-earthquake damage assessment predominantly utilize deep learning methods and InSAR-based Damage Proxy Maps. However, these approaches require data of high quality, with both multi-temporal and spatiotemporal resolution, and are heavily reliant on supervised learning, limiting their applicability on a broader scale. This paper presents a Case-Based Reasoning Framework Augmented with Causal Graph Bayesian Networks for Multi-Hazard Impact Assessment. This method demonstrates strong adaptability to noisy data, making it an innovative tool in the field of earthquake damage estimation. We applied this framework to analyze the catastrophic earthquakes that struck Turkey and Japan in 2023 and 2024, respectively, using them as bases for our case-based reasoning process. For the Turkey case, our model achieved a precision of 99.9%, a recall of 40.2%, and an F1 score of 57.4% in detecting landslides—significantly surpassing the performance of the USGS a priori model. In detecting liquefaction, the model showed a recall of 95.9% and an F1 score of 70.6%, both substantial improvements over the preliminary model. For the 2024 Noto Peninsula earthquake, our method enhanced the Area Under the Curve (AUC) index from 0.73 to 0.77, further validating the effectiveness of our approach. This study offers a highly precise, scalable, and unsupervised learning method for estimating earthquake disaster damage, providing a valuable asset for optimizing post-disaster resource allocation, reducing economic losses and accurately repairing the environment.