基于CatBoost的预测斜坡稳定性早期预警模型
Yuan Cai1, Ying Yuan2, Aihong Zhou3
1School of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang, 050031, China.
Scientific reports
|October 29, 2024
概括
一个新的分类提升 (CatBoost) 模型使用六个特征准确预测斜率稳定性. 这种先进的模型提供了可靠的倾斜不稳定的早期警告,优于其他机器学习方法.
科学领域:
- 地质技术工程 地质技术工程
- 机器学习应用 机器学习应用
背景情况:
- 斜坡的稳定性对于基础设施安全至关重要.
- 需要准确的预测模型来减轻与斜坡失败相关的风险.
研究的目的:
- 开发和评估一种用于预测斜率稳定的新型机器学习模型.
- 建立一个关于斜坡不稳定的早期预警系统.
主要方法:
- 使用六个斜率特征开发了一个分类提升 (CatBoost) 模型.
- CatBoost模型使用了一个对称的树底,有序的提升.
- 性能与支持向量机 (SVM),光梯度增强机 (LGBM),随机森林 (RF) 和后勤回归 (LR) 进行了比较,使用了五个指标.
主要成果:
- CatBoost模型实现了100%的精度和0.95.9的曲线下面积 (AUC).
- 它显示了训练和测试集之间的低准确度差异 (6.25%),表明有效的过拟合缓解.
- 该模型的预测使得建立一个可靠的斜率不稳定警告系统成为可能.
结论:
- CatBoost模型为斜坡稳定性评估提供了卓越的预测准确性和稳定性.
- 开发的早期预警系统为实际的风险管理提供了有价值的分类.
- 这种方法提高了地理危险预测的研究和实际应用.
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