TY - DATA
T1 - Data underlying the publication: Optimizing multi-resolution topographic indicator combinations for debris flow susceptibility assessment based on factorial experiments and UMAP analysis
PY - 2025/10/31
AU - Si Alu
AU - Quan Lai
AU - Enliang Guo
AU - Yongfang Wang
AU - Jiquan Zhang
UR - 
DO - 10.4121/6a777ec0-92a7-4818-ab89-b1234c5c1f80.v1
KW - debris flow susceptibility
KW - optimal pixel resolution combination
KW - factorial experiment
KW - UMAP
KW - Machine learning
N2 - <p>This dataset 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 dataset contains the basic training and testing sets used in this study to assess debris flow susceptibility.</p>
ER -