## 6 Conclusions  
The regional LSA and its associated uncertainties remain significant challenges for managing and mitigating landslide risks. This study focused on Badong County and examined the impact of model selection uncertainty and non-landslide dataset sampling strategies on the susceptibility zoning performance. The findings revealed that selecting non-landslide samples from the slope units without landslides substantially improved the accuracy of LSM compared to selecting those outside a designated buffer zone. The confusion matrix metrics and AUC values consistently demonstrated superior performance across the four machine learning models when the slope unit sampling strategy was applied, with the RF model achieving the highest accuracy. The AUC values for the four models showed improvements ranging from 0.011 to 0.065 between the two sampling strategies. Furthermore, using very-low-susceptibility zones identified by nonlinear machine learning models as non-landslide samples enhanced the susceptibility modeling performance, with the effectiveness depending on the accuracy of the pre-classified models. The higher-performing pre-classified models yielded better-performing non-landslide samples. These results suggested that the common strategy of randomly selecting non-landslide samples outside the buffer zone introduced significant uncertainty. Integrating the outputs of various machine learning models using an average method can effectively enhance predictive performance. Metrics such as the AUC and FR confirmed that the averaging method provided a comprehensive analysis of susceptibility outputs from different models, thereby reducing the uncertainty associated with model selection. This study confirmed that the uncertainties in LSA stemmed in part from the dataset sampling strategies and selection of machine learning models. However, these uncertainties were mitigated by enhancing the quality of non-landslide samples and integrating the average susceptibility indices from multiple models. Accordingly, susceptibility modeling frameworks should adopt robust and well-justified sampling strategies to ensure more reliable results. In addition, utilizing multiple assessment models further minimized the uncertainty associated with the model selection. Future research should focus on examining the effects of different sampling strategies for landslide and non-landslide samples and exploring the influence of varying data resolutions on susceptibility modeling results. Formal analysis, W.J. ; data curation, W.J. ; writing--original draft preparation, W.J. ; writing--editing, L.L. ; supervision, R.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Digital Elevation Model (DEM) data: the original data presented in this study are openly available on Geospatial Data Cloud website at [[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/)) (accessed on 12-July-2024). Basic geological data: the original data presented in this study are openly available in the National 1:200,000 Digital Geological Map (Public Edition) spatial database at [[http://dcc.ngac.org.cn/cn/page/index](http://dcc.ngac.org.cn/cn/page/index)]([http://dcc.ngac.org.cn/cn/page/index](http://dcc.ngac.org.cn/cn/page/index)) (accessed on 12-July-2024). Land use data and normalized difference vegetation index (NDVI): the original data presented in this study are openly available from the United States Geological Survey at earthexplorer.usgs.gov (accessed on 18-July-2024). Vector data: the original data presented in this study are openly available in the National Geographic Information Resources Catalogue Service System at [[https://www.webmap.cn/](https://www.webmap.cn/)]([https://www.webmap.cn/](https://www.webmap.cn/)) (accessed on 12-July-2024). Rainfall data: restrictions apply to the availability of these data. Data were obtained from the China Geological Survey, and permission was obtained from the China Geological Survey.