## 1 Introduction
An ROC (receiver operating characteristic, see Section 3.5) curve analysis revealed that the model had very good predictive ability with an area under the ROC curve of 98%. The main variables affecting the model were the slope, elevation, slope orientation, mean annual precipitation, lithology, and land use. LG was also applied [20] for developing a landslide forecasting model at the local level triggered by earthquakes in Taiwan, showing LG to be effective in predicting the spatial distribution of the probability of earthquake-triggered landslides. The main variables affecting the model were PGA, roughness, lithology, the interactions between PGA and roughness, and PGA and lithology. The slope was not statistically significant in this study because roughness is an important factor for slope stability. Random forest (RF) is used and accepted in many disciplines, including geology, for landslide assessment [21]. Its superiority has been demonstrated in the susceptibility analyses of landslides worldwide [22]. RF was used [22] to carry out a global susceptibility assessment, showing that the RF model has excellent predictive ability with an area under the ROC curve value of 98.5%, as well as good generalization and robustness. The results show that the relative importance of conditioning factors varies depending on the landslide event, although distance to faults, earthquake intensity, elevation, and slope are important factors in most events. In terms of artificial neural network (ANN), several statistical results on landslide susceptibility in different areas of Brazil were presented [23]. The study obtained 100% correct classification results without any false positives or false negatives. The results of the ANN application showed that the profile curvature, topographic wetness index, land use, and slope were the factors that most influenced landslide susceptibility. ANN was also used in [24] for a geospatial database based on 217 landslide events and nine conditioning factors in Vietnam. The study implemented different types of ANN, including an MLP (multilayer perceptron) model, which correctly classified around 84% of landslides. The MLP model had good performance in terms of goodness of fit with the training dataset, obtaining an RMSE (root mean square error) of 0.331. The study also indicated that the distance to roads and rivers was the most influential factor in landslide occurrence. A comparison of different machine learning models, including support vector machine (SVM), for evaluating landslide susceptibility in Serbia was conducted in [25]. The study highlighted SVM as the best-performing model, as it outperformed the other models in all the evaluation measures. Similarly, various methods and techniques used for mapping landslide susceptibility were discussed [26]. The best-performing model proposed was SVM with cross-validation, which achieved an area under the ROC curve value of 85% in the best combination of parameters for the radial basis function (RBF) kernel. The authors showed that the use of geospatial information, along with SVM models, allows reliable models to assess landslide susceptibility. Considering the wide variety of statistical methods and the various results mentioned above, in this paper, different machine learning techniques (logistic regression, random forest, artificial neural network, and support vector machine) are used and applied to the case of slope instabilities caused by the Lorca earthquake. This approach helps to obtain a more accurate understanding of the factors that trigger slope instabilities and to perform and validate the resulting seismically induced landslide susceptibility maps. Such models are also applied to the data resulting from the dimensionality reduction in the variables using the principal component analysis technique. The results of both approaches are compared with those obtained in the same study area using physical-based methods [27] to identify the best method for co-seismic landslide mapping.