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<p>The Great Wall, the world’s largest linear cultural heritage, faces rapid erosion due to natural conditions. Erosion damage starting from the wall’s base can lead to structural instability and collapse, posing the most destructive threat. This study conducts the first digital survey of erosion damage to the Ming Great Wall in Gansu using drone. Erosion damage was quantified through kernel density and spatial autocorrelation analysis. An optimal XGBoost machine learning model was developed to assess the contributions of 14 environmental factors. Combining SHAP value decomposition and partial dependence plots (PDP) revealed nonlinear interactions among these factors. Significant spatial aggregation of erosion damage was observed with salinization, topographic fluctuation, and annual average precipitation identified as primary influencing factors. Each factor exhibited either promoting or inhibiting effect. This study enhances understanding of the spatial dynamics driving mechanism of erosion on the Great Wall, providing a scientific basis for its conservation and restoration.</p>

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Explainable machine learning links erosion damage to environmental factors on Gansu rammed earth Great Wall

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月25日

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  • Tianlian Wang,
  • Mengdi Zhang,
  • Zhe Li

摘要

The Great Wall, the world’s largest linear cultural heritage, faces rapid erosion due to natural conditions. Erosion damage starting from the wall’s base can lead to structural instability and collapse, posing the most destructive threat. This study conducts the first digital survey of erosion damage to the Ming Great Wall in Gansu using drone. Erosion damage was quantified through kernel density and spatial autocorrelation analysis. An optimal XGBoost machine learning model was developed to assess the contributions of 14 environmental factors. Combining SHAP value decomposition and partial dependence plots (PDP) revealed nonlinear interactions among these factors. Significant spatial aggregation of erosion damage was observed with salinization, topographic fluctuation, and annual average precipitation identified as primary influencing factors. Each factor exhibited either promoting or inhibiting effect. This study enhances understanding of the spatial dynamics driving mechanism of erosion on the Great Wall, providing a scientific basis for its conservation and restoration.