A liquefaction occurrence model for regional analysis

Liquefaction causes damage and economic losses that can exceed the impact caused by ground shaking in earthquakes. However, probabilistic models to predict liquefaction occurrence on a regional scale are scarce and uncertain. We developed a non-parametric model using a database with more than 40 events worldwide. We trained and tested a supervised machine-learning model to predict liquefaction occurrence and non-occurrence, using a well-established methodology to select the optimal explanatory variables that correlate best with liquefaction occurrence. The optimal variables include strain proxy, slope, topographic roughness index, water-table depth, average precipitation, and distance to the closest water body. We compared the proposed model with existing proposals from the literature using the area under the Receiver Operating Characteristic (ROC) curve and the Brier score. Lastly, we apply the proposed model to assess liquefaction occurrence for one historical event and two hypothetical scenarios in Montenegro and Albania.

Automated summary

A non-parametric model using machine learning was developed to predict liquefaction occurrence, and its performance was evaluated by comparing the area under ROC curve and Brier score with existing models. The proposed model was applied to assess liquefaction occurrence for historical events and hypothetical scenarios in Montenegro and Albania.