使用回归 Kriging 的稀疏数据对高速公路次级的地面结算预测
Lei Huang1, Wei Qin2,3, Guo-Liang Dai4
1College of Architecture and Civil Engineering, Sanming University, Sanming, 365004, China.
Scientific reports
|October 19, 2024
概括
通过回归Kriging (RK) 方法改进了使用稀疏数据预测高速公路地下层结算. 整合Box-Cox转换提高了准确性,优于传统方法和神经网络,可靠地盘定位预测.
科学领域:
- 地质技术工程 地质技术工程
- 土木工程 土木工程是指土木工程.
- 环境工程 环境工程
背景情况:
- 在基础设施项目中,准确地预测高速公路次级的地面沉积情况至关重要.
- 稀少的样本数据经常挑战现实世界的场景中的预测准确性,这是以前未被充分探索的问题.
- 现有的方法因数据稀缺而困难,需要新的方法来可靠地预测结算.
研究的目的:
- 提出和评估一种基于回归Kriging (RK) 的方法,用于使用稀疏数据进行增强的地面沉积预测.
- 调查Box-Cox转换和趋势结构对RK预测准确性的影响.
- 将拟议的RK方法与经典技术和反向传播神经网络 (BPNN) 的性能进行比较.
主要方法:
- 一个回归Kriging (RK) 框架被用于地面结算预测.
- 使用Box-Cox转换来实现样品残留的静态性,从而改善预测.
- 一个第一阶多项式趋势结构被确定为首要整合结算预测的最佳.
主要成果:
- 整合Box-Cox转换显著降低了评估指标 (RMSE,MAE,MAAPE,SCI),提高了使用稀疏数据的预测准确度.
- 第一个顺序的多项式趋势结构被证明比较高的订单更适合初级整合结算.
- 拟议的RK方法与经典方法和BPNN相比显示出更高的准确性,由于数据限制而表现不佳.
结论:
- 通过Box-Cox转换和一阶多项式趋势增强的回归Kriging (RK) 方法,为使用稀疏数据进行地面结算预测提供了高度准确的解决方案.
- 这种方法在具有有限数据的场景中显著改进了传统方法和反向传播神经网络 (BPNN).
- 这些发现强调了数据转换和适当的趋势建模对于强大的地质技术预测的重要性.
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