Abstract: In order to comprehensively assess and predict earthquake damage losses, a method for probability density prediction is proposed. First, the life-year loss was obtained through the improved lifeyears loss calculation method. Secondly, by use of stepwise regression analysis which is based on Akaike information criterion (AIC), the strongly correlated factors of lifeyears loss were identified and furthermore QRNN model was constructed. Then the nonlinear relationship between the predicted value of lifeyears loss and the strongly correlated factors was obtained, the predicted loss under different quantile points was outputted, and the lifeyears loss probability density was predicted by adopting Gaussian kernel function. Finally, with the damage loss data of 189 Chinese earthquakes from 1996 to 2014 as training samples, the lifeyears loss of 10 earthquakes in 2015 was predicted and compared with quantile regression B-spline (QRBS) model and three linear models. The results show that the damage loss probability density prediction based on the proposed model reduces data dependency while improves evaluation efficiency, and the average absolute error of the prediction is less than 7.5%, which is effective for damage assessment.

Key words: lifeyears loss, stepwise regression, quantile regression neural network (QRNN), correlated factors identification, probability density function