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

DOI:10.4121/6a777ec0-92a7-4818-ab89-b1234c5c1f80.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/6a777ec0-92a7-4818-ab89-b1234c5c1f80

Datacite citation style

Alu, Si; Lai, Quan; Guo, Enliang; Wang, Yongfang; Zhang, Jiquan (2025): Data underlying the publication: Optimizing multi-resolution topographic indicator combinations for debris flow susceptibility assessment based on factorial experiments and UMAP analysis. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/6a777ec0-92a7-4818-ab89-b1234c5c1f80.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

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.

History

  • 2025-10-31 first online, published, posted

Publisher

4TU.ResearchData

Format

ArcGIS/.shp

Funding

  • Natural Science Foundation of Inner Mongolia Autonomous Region (grant code Grant No. 2024QN04005)
  • Research Program of Science and Technology at Universities of Inner Mongolia Autonomous Region (grant code Grant No. NJZZ23018)
  • Fundamental Research Funds for Inner Mongolia Normal University (grant code Grant No. 2023JBQN043)
  • Scientific Research Foundation for Advanced Talents of Inner Mongolia Normal University (grant code Grant No. 2021YJRC009)

Organizations

Inner Mongolia Normal University, College of Tourism

DATA

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