用表格基础模型生成滑坡数据，解决样本少且不均衡问题
Accurate and Robust Generative Approach for Overcoming Data Sparsity and Imbalance in Landslide Modeling with A Tabular Foundation Model
- 基于表格基础模型学习有限样本中的多变量依赖关系
- 在20个滑坡数据集上生成数据与真实分布高度一致
- 适合数据稀疏场景下的滑坡风险评估与建模
滑坡研究依赖于充足的观测数据，但现有滑坡清单常存在数据稀疏和不平衡问题，影响对触发条件和失稳机制的理解。数据生成可有效捕捉有限观测中的特征关联。然而，现有生成方法难以建模复杂特征关系，且跨场景鲁棒性不足。本文提出一种基于表格基础模型的多特征滑坡数据生成方法，利用其从少量观测中学习的能力，有效保留滑坡发生中的多变量依赖与统计特性。在20个滑坡数据集上的对比实验表明，生成数据与真实分布高度吻合，保持了合理的特征依赖，并在不同环境条件下表现出强鲁棒性。该方法为克服数据稀疏与不平衡问题提供了有效手段，增强了有限观测下的滑坡易发性建模与风险评估能力。
原文摘要 · Abstract (English)
Landslide investigation relies on sufficient and well-balanced observational data influenced by geological, hydrological, and anthropogenic factors. Available landslide inventories are often sparse and imbalanced, which limits understanding of triggering conditions and failure mechanisms. Data generation provides an effective approach to help capture feature dependencies from limited landslide observations. However, existing generation approaches for landslides often struggle to capture complex relationships among features and lack robustness across multiple scenarios and interacting factors. Here, we propose an accurate and robust approach for generating multi-feature landslide datasets by utilizing a tabular foundation model. By leveraging the capacity to learn from limited observations, the proposed approach effectively preserves the multivariate dependencies and statistical characteristics inherent in landslide occurrences. Comparative experiments on 20 landslide inventories demonstrate that the generated datasets closely align with observed distributions, maintain realistic feature dependencies, and exhibit robustness across different environmental contexts. This work provides an effective approach to overcome data sparsity and imbalance and strengthens landslide susceptibility modeling and risk assessment under limited observations.
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