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Currently, some catastrophe models used by the insurance industryaccount for liquefaction by applying a simple factor to shaking-inducedlosses. The factor is based only on local liquefaction susceptibility andthis highlights the need for a more sophisticated approach to incorporatingthe effects of liquefaction in loss models. This study compares 11 uniquemodels, each based on one of three principal simplified liquefactionassessment methods: liquefaction potential index (LPI) calculated fromshear-wave velocity, the HAZUS software method and a method createdspecifically to make use of USGS remote sensing data. Data from theSeptember 2010 Darfield and February 2011 Christchurch earthquakes in NewZealand are used to compare observed liquefaction occurrences to forecastsfrom these models using binary classification performance measures. Theanalysis shows that the best-performing model is the LPI calculated usingknown shear-wave velocity profiles, which correctly forecasts 78 % ofsites where liquefaction occurred and 80 % of sites where liquefactiondid not occur, when the threshold is set at 7. However, these data may notalways be available to insurers. The next best model is also based on LPIbut uses shear-wave velocity profiles simulated from the combination of USGSVS30 data and empirical functions that relate VS30 toaverage shear-wave velocities at shallower depths. This model correctlyforecasts 58 % of sites where liquefaction occurred and 84 % of siteswhere liquefaction did not occur, when the threshold is set at 4. Thesescores increase to 78 and 86 %, respectively, when forecasts are based onliquefaction probabilities that are empirically related to the same values ofLPI. This model is potentially more useful for insurance since the input dataare publicly available. HAZUS models, which are commonly used in studieswhere no local model is available, perform poorly and incorrectly forecast87 % of sites where liquefaction occurred, even at optimal thresholds.This paper also considers two models (HAZUS and EPOLLS) for estimation of thescale of liquefaction in terms of permanent ground deformation but finds thatboth models perform poorly, with correlations between observations andforecasts lower than 0.4 in all cases. Therefore these models potentiallyprovide negligible additional value to loss estimation analysis outside ofthe regions for which they have been developed

Evaluating simplified methods for liquefaction assessment for loss estimation