Evaluating Conditioning Factor Selection and Reduction for Landslide Susceptibility Mapping in the Hualong–Xunhua Region, Upper Yellow River

Conditioning factor reduction can decrease redundancy and modelling complexity in landslide susceptibility mapping. These factors describe the environmental conditions associated with slope instability. Statistical screening, redundancy diagnostics, importance rankings and internal validation do not establish that the remaining conditioning factors can be safely removed. Whether aggressive reduction preserves the environmental information required for reliable regional landslide susceptibility mapping therefore remains uncertain. This study developed an integrated framework for evaluating the reliability of conditioning-factor reduction in the Hualong–Xunhua region of the Upper Yellow River, China, combining statistical diagnostics, model interpretation, GeoDetector analysis, and random and spatial validation. The compact system selected during development was further compared with the complete system using a reserved evaluation subset, followed by regional susceptibility mapping and spatial comparison. The results showed that, first, precipitation showed weak univariate but strong model and interaction evidence, illustrating their complementarity. Second, Top5, comprising normalised difference vegetation index (NDVI), distance to rivers, precipitation, lithology and terrain ruggedness index (TRI), met development criteria: mean area under the receiver operating characteristic curve (AUC) values were 0.866 under random validation and 0.846 under spatial validation, compared with 0.875 and 0.853 for the complete 17-factor system (Full17), respectively. Third, in the post-selection reserved comparison, mean AUC was 0.865 for Top5 and 0.895 for Full17, with paired uncertainty supporting this decline; favourable development performance did not ensure reliable substitution. Finally, Full17 was retained for final mapping; absolute score differences greater than 0.10 covered 32.02% of mapped area, indicating potential spatial information loss. Similar predictive performance did not imply spatial equivalence. These findings show that factor reduction requires evaluation of predictive performance, generalisation, model dependence, and spatial information preservation.