Supplemental Material underlying the publication: Optimizing multi-resolution topographic indicator combinations for debris flow susceptibility assessment based on factorial experiments and UMAP analysis

This supplemental material supports the study "Optimizing multi-resolution topographic indicator combinations for debris flow susceptibility assessment based on factorial experiments and UMAP analysis". It leveraging a factorial experimental design and UMAP analysis to systematically evaluated nearly 350,000 prediction results from RF, GBDT, and BPNN models. Then a multi-faceted assessment across seven dimensions revealed the impact of resolution combinations on susceptibility and identified each model's optimal combination. The supplemental material 1 and 2 includes seven evaluation indices data of the debris flow susceptibility prediction results for all treatment combinations, and the supplemental material 3 and 4 includes Probability 1 (AUC ranked in the top 1000) and Probability 2（remaining six indices all ranked in the top 15%) data of each treatment combinations.

本补充材料支撑题为《基于析因实验与均匀流形近似与投影（UMAP）分析的泥石流易发性评价多分辨率地形指标组合优化》的研究。本补充材料采用析因实验设计与UMAP分析，对随机森林（RF）、梯度提升决策树（GBDT）及反向传播神经网络（BPNN）三类模型生成的近35万组预测结果开展了系统评估。随后通过覆盖七大维度的综合评估，揭示了分辨率组合对泥石流易发性评价的影响，并确定了各模型的最优分辨率组合。补充材料1与补充材料2收录了所有处理组合的泥石流易发性预测结果对应的七项评价指标数据；补充材料3与补充材料4则收录了各处理组合的概率1（AUC排名前1000）与概率2（其余六项指标均排名前15）数据。