Impact of orthogonal transformation for factors on model performance in landslide susceptibility

Considering the correlation among the conditioning factors, this study explores the impact of the orthogonal transformation for factors on model performance, with the LR model. Landslide inventory and factor theme maps were first constructed and used for factor analysis. Subsequently, effective factors with high correlation were applied for orthogonal transformation, to obtain the reconstructed factors. Meanwhile, reconstructed factors and the remaining effective factors were jointly involved in the model construction. Finally, differences in model performance before and after the transformation were discussed, in terms of the predictive ability and computational ability. Results indicate that the modified model maintains high predictive ability compared with the initial model, i.e., high values of AUC (0.939), specificity (0.956), sensitivity (0.977), and accuracy (0.968) in the training phase, and AUC (0.928), specificity (0.791), sensitivity (0.866), and accuracy (0.849) in the test phase. Besides, the modified model not only exhibits high operation speed but also smaller standard errors of the regression coefficients compared with the initial model, overall. The high predictive and computational performance of the modified susceptibility model provides a window for optimization of the landslide susceptibility in the modeling workflow.