Prediction of landslide displacement based on GA-LSSVM with multiple factors

This paper presents a new model for predicting the displacement of a landslide based on the least-squares support vector machine (LSSVM) with multiple factors and a genetic algorithm (GA) is used to optimize the parameters of the LSSVM model. First, based on original monitoring displacement data, single factor GA-LSSVM models are established with and without wavelet decomposition. Second, from the analysis of the basic characteristics of a landslide, the main influencing factors of landslide displacement are identified according to their correlation coefficients. A multifactor GA-LSSVM model is then established for the prediction of landslide displacement. A case study of a landslide reveals that wavelet decomposition can efficiently improve the prediction accuracy of the GA-LSSVM model. In addition, the multifactor GA-LSSVM model performs consistently better than the single factor models for the same measurements.