Wavelet neural network has better approximation and fault-tolerance for combining the time-frequency localization of wavelet transform and self-study function of traditional neural network. We took some typical landslides in hydropower engineering region as an example and built three wavelet neural net-work models of multiple factors for landslide deformation prediction

on the basis of analyzing basic charac-teristics and the relationships between landslide deformation and main influencing factors of the landslide. By analyzing and comparing the results of the models

we found that the wavelet neural network model including the two factors (displacement rate and rainfall) has the highest prediction accuracy in the three models.