Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model

2026-04-28

AI Summary

This study addresses the challenge of landslide susceptibility prediction in data-scarce regions such as mountainous and high-altitude areas, where traditional data-driven approaches suffer from limited performance due to insufficient labeled samples. The authors propose a novel knowledge–data dual-driven paradigm that, for the first time, integrates geomorphological prior knowledge with a tabular foundation model through joint learning to achieve high-accuracy susceptibility modeling. This approach substantially reduces reliance on large-scale annotated datasets, attaining baseline performance using only 30% of the landslide samples in central Italy. Furthermore, it demonstrates strong generalization and reliability when successfully applied to the Qilian permafrost region of the Tibetan Plateau, confirming its effectiveness under extreme data scarcity.

Abstract

Landslide susceptibility prediction is critical for geohazard risk assessment and mitigation. Conventional data-driven paradigm achieves high predictive accuracy but require sufficient conditioning factors and large-scale landslide inventories. However, in practical engineering applications across mountainous and plateau regions, data-scarce conditions are commonly observed, where such data requirements are rarely satisfied, rendering conventional data-driven paradigm inapplicable. To address this issue, we propose a knowledge-data dually driven paradigm for accurate landslide susceptibility prediction under data-scarce conditions. The essential idea behind the proposed novel paradigm is the integration of the geomorphic prior knowledge with scarce landslide data. To validate the proposed paradigm, we first applied it to a data-rich region in central Italy, where a conventional data-driven paradigm trained on the full dataset served as the baseline. By utilizing only 30% of the available landslide data, the proposed paradigm achieved comparable predictive accuracy to the baseline, demonstrating its effectiveness under data-scarce conditions. The paradigm was further evaluated in a genuinely data-scarce environment for application, the Qilian Permafrost Region of the Tibetan Plateau, where it also yielded reliable susceptibility predictions, confirming its applicability under data-scarce conditions.

Problem

• landslide susceptibility prediction
• data-scarce conditions
• geohazard risk assessment
• conditioning factors
• landslide inventory

Innovation

• knowledge-data dually driven
• geomorphic priors
• tabular foundation model
• data-scarce conditions
• landslide susceptibility prediction