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

表格基础模型在数据稀疏条件下改进滑坡预测

作者 PulseAugur 编辑部 · [4 个来源] ·

两篇新研究论文提出了利用表格基础模型进行滑坡易发性预测的新方法，解决了数据稀疏和不平衡的常见问题。第一篇论文介绍了一种生成方法，用于创建真实的滑坡数据集，保留了复杂特征依赖性，并在各种场景中展示了鲁棒性。第二篇论文提出了一种知识-数据双驱动范式，将地貌先验知识与有限的滑坡数据相结合，实现了与需要更多数据的传统方法相当的准确性。 AI

影响 这些方法可以通过提高滑坡易发性模型在数据稀疏地区的准确性，显著改善地质灾害评估。

排序理由 两篇发表在arXiv上的学术论文，提出了使用表格基础模型进行滑坡预测的新方法。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →

表格基础模型在数据稀疏条件下改进滑坡预测

报道来源 [4]

  1. arXiv cs.LG TIER_1 English(EN) · Kaixuan Shao, Gang Mei, Yinghan Wu, Nengxiong Xu, Jianbing Peng ·

    Accurate and Robust Generative Approach for Overcoming Data Sparsity and Imbalance in Landslide Modeling with A Tabular Foundation Model

    arXiv:2604.25159v1 Announce Type: new Abstract: 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 under…

  2. arXiv cs.LG TIER_1 English(EN) · Yuting Yang, Gang Mei, Feng Chen, Yongshuang Zhang, Jianbing Peng ·

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

    arXiv:2604.25196v1 Announce Type: new 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 …

  3. arXiv cs.LG TIER_1 English(EN) · Jianbing Peng ·

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

    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 a…

  4. arXiv cs.LG TIER_1 English(EN) · Jianbing Peng ·

    Accurate and Robust Generative Approach for Overcoming Data Sparsity and Imbalance in Landslide Modeling with A Tabular Foundation Model

    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 me…

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