Uncertainties in landslide susceptibility prediction modeling: A review on the incompleteness of landslide inventory and its influence rules

Landslide inventory is an indispensable output variable of landslide susceptibility prediction (LSP) modelling. However, the influence of landslide inventory incompleteness on LSP and the transfer rules of LSP resulting error in the model have not been explored. Adopting Xunwu County, China, as an example, the existing landslide inventory is first obtained and assumed to contain all landslide inventory samples under ideal conditions, after which different landslide inventory sample missing conditions are simulated by random sampling. It includes the condition that the landslide inventory samples in the whole study area are missing randomly at the proportions of 10%, 20%, 30%, 40% and 50%, as well as the condition that the landslide inventory samples in the south of Xunwu County are missing in aggregation. Then, five machine learning models, namely, Random Forest (RF), and Support Vector Machine (SVM), are used to perform LSP. Finally, the LSP results are evaluated to analyze the LSP uncertainties under various conditions. In addition, this study introduces various interpretability methods of machine learning model to explore the changes in the decision basis of the RF model under various conditions. Results show that (1) randomly missing landslide inventory samples at certain proportions (10%–50%) may affect the LSP results for local areas. (2) Aggregation of missing landslide inventory samples may cause significant biases in LSP, particularly in areas where samples are missing. (3) When 50% of landslide samples are missing (either randomly or aggregated), the changes in the decision basis of the RF model are mainly manifested in two aspects: first, the importance ranking of environmental factors slightly differs; second, in regard to LSP modelling in the same test grid unit, the weights of individual model factors may drastically vary.