Landslides are one of the most frequent and devastating natural disasters around the world with intensifying impacts on human lives and the environment. To effectively deal with landslides and their consequences, it is primarily important to demarcate areas susceptible to landslides. This can be done through landslide susceptibility mapping (LSM). In this study, a novel approach for landslide susceptibility mapping based on the uncertain and parameter-free density-based clustering (UPFDBSCAN) algorithm was proposed. It merges the ideas from the dominant set clustering algorithm, the DBSCAN algorithm, and the uncertain data modeling method. The study aims to overcome the limitations of depending on user-defined density parameters, the inability to identify clusters of varied densities, and to model the uncertain data, in the DBSCAN algorithm and most of the existing clustering algorithms. This improves the clustering accuracy and efficiency for LSM modeling. For this purpose, the proposed model was experimented with an inventory containing 506 samples of landslide and non-landslide locations, and data of 7 landslide influencing factors from the Baota District in Shaanxi, China. The model’s performance was evaluated and compared with existing clustering-based LSM models as state-of-the-art methods based on standard evaluation metrics. The results revealed that the proposed model obtained the highest performance (sensitivity = 0.935, specificity = 0.944, accuracy = 0.939, AUC = 0.881, JC = 0.898, and purity = 0.899) and it was thus superior to the other models. This study's findings can help decision-makers, policymakers, and land stakeholders to implement significant strategies with early warning systems to predict, prevent, and mitigate the occurrence and impacts of landslides.

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