A knowledge-data dual paradigm using geomorphic priors and a tabular foundation model achieves baseline-level landslide susceptibility prediction accuracy with only 30% of typical data in tested regions.
author Guzzetti, F
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Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model
A knowledge-data dual paradigm using geomorphic priors and a tabular foundation model achieves baseline-level landslide susceptibility prediction accuracy with only 30% of typical data in tested regions.