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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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一个基于机器学习和SHAP解释技术的可解释 (可解释) 模型,用于绘制风侵蚀危险的地图.

Hamid Gholami1, Ehsan Darvishi2, Navazollah Moradi2

  • 1Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas,, Hormozgan, Iran. hadesertt64@gmail.com.

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科学领域:

  • 环境科学 环境科学
  • 地质科学 地质科学
  • 数据科学数据科学数据科学

背景情况:

  • 风侵蚀是全球干旱地区的重大威胁,特别是在中东和伊朗.
  • 风侵蚀危险地图对于识别高风险地区和实施减缓策略至关重要.

研究的目的:

  • 开发一种可解释的机器学习 (ML) 模型,用于绘制风侵蚀危害的地图.
  • 确定影响风侵蚀的关键特征,并使用沙普利增量扩展 (SHAP) 解释预测模型的输出.

主要方法:

  • 采用了四种ML模型:随机森林 (RF),支持向量机 (SVM),极端梯度增强 (XGB) 和二次差异分析 (QDA).
  • 使用多变量自适应回归线 (MARS) 进行特征选择和变量膨胀因子 (VIF) 评估多线性.
  • 应用SHAP用于模型解释性和特征重要性分析.

主要成果:

  • 八个特征,包括海拔,土壤体密度,降水,面积,斜率,土壤砂含量,植被覆盖 (NDVI) 和石质,被确定为最有效的.
  • 射频模型强调高度和土壤质量密度是最重要的特征.
  • 所有的ML模型都显示出高精度 (AUROC>90%,PR>90%),SVM的表现略好一些. SVM结果显示中等,高和非常高风侵蚀危险类别,分别覆盖霍尔莫兹甘省的20.9%,23%和16.6%.
  • SHAP分析证实了土壤的沙含量和海拔高度是模型预测的主要贡献者.

结论:

  • 这项研究开创了可解释的ML模型的应用,用于绘制伊朗南部风侵蚀危险的地图.
  • 高度和土壤特性是风侵蚀的关键因素.
  • 纳入模型可解释性对于更深入地了解环境研究中的预测模型输出至关重要.