## 5 Conclusions  
Landslides, being the most critical natural hazard, are a very serious threat to human life, infrastructure, and natural resources across the globe. The identification of an accurate and stable ML model for predicting landslide susceptibility is often a very difficult and demanding task, especially when applied to a region with rugged terrain. In this study, we implemented five state-of-the-art ML algorithms, i.e., RF, SVM, maxENT, GBM, and LR, to investigate their performances for LSM in part of northern Pakistan. The study indicated that:  
* The 12 selected influencing factors (i.e., elevation, slope, aspect, TWI, TPI, distance to drainage, distance to fault, distance to road, NDVI, rainfall, LCLU, and geological layer) were useful when scanned through the IGR test, and none of the LIFs had a multicollinearity problem. * The extracted 70% of the sample data used for the tuning and training of the models showed a good agreement with the tuning parameters, which helped in achieving higher accuracies during the training process. * Apart from predicting large-scale landslides, a visual analysis (Figure 12) of modeled susceptibility maps indicated that all five models were also able to predict very-small-scale landslides, which can provide a better understanding to identify landslide risks in the region. * The descriptive analysis of the LIF contribution showed that the area lying in the elevation range of 505 to 3895 m, with NE-WSW facing slopes, near the drainage networks, with LCLU other than snow, dense vegetation, cultivated land, and water bodies could be attributed directly to the slope failure manifestation in the study area, which can experience a relatively higher chance of landslide occurrences in the future. * During the validation process, using 30% of the samples, the results showed that SVM (AUC = 0.969, POA = 2669), RF (AUC = 0.967, POA = 2656), and GBM (AUC = 0.967, POA = 2623) were the top three performers, leaving behind maxENT (AUC = 0.872, POA = 1761) and LR (AUC = 0.836, POA = 1299). The validation through the calculation of other parameters (precision, recall, ACC, F1 score, and MCC) showed a similar trend in terms of the performance sequence of the models. * Exhibiting minimal differences in terms of all the evaluated parameters with SVM and RF, the study also found the better performance of the GBM model for LSM in the region. * SVM > RF > GBM > maxENT > LR represents the overall performance ranking with decreasing POA values. * Conclusively, these modeled results can be helpful in understanding and assessing the scope of landslide problems in this area and can also provide help in risk reduction and mitigation measures in this region as well as in other parts of the globe with similar topographical, climatic, and geological conditions. values of GBM, Figure S4: Changing trends in parametric values of maxENT for different feature types, Figure S5: Changing trends in parametric values of decay for LR model. All authors contributed to the manuscript and discussed the results. This research was supervised by X.D. ; conceptualization, N.S. and X.D. ; data curation and methodology, N.S. ; formal analysis, N.S. and S.A.; funding acquisition, X.D. All authors have read and agreed to the published version of the manuscript. The first author was supported by a Hong Kong Ph.D. Fellowship of the Research Grants Council (RGC) of Hong Kong. The work was supported by the RGC (Grants Numbers: PolyU152164/18E and PolyU152233/19E); the Research Institute for Sustainable Urban Development (RISUD); and the Innovation and Technology Fund. Not applicable. The data and other related scripts that support the findings of this study are available on demand from the corresponding author. The authors wish to acknowledge the Research Grants Council (RGC) of Hong Kong, the Research Institute for Sustainable Urban Development (RISUD) of the Hong Kong Polytechnic University, and the Innovation and Technology Fund (ITF) for their support in this research.