通过使用新型混合组合数据驱动模型,对冲堤周围的冲孔深度进行近似估计.
Balraj Singh1, Vijay K Minocha2
1Civil Engineering Department, Delhi Technological University, Delhi 110042, India
概括
本研究介绍了一种混合机器学习模型,用于准确预测堤附近的扫地深度,增强河岸稳定性评估. 开发的模型,包装-添加回归-随机树 (B-AR-RT),为复杂的扫描现象提供了可靠的解决方案.
科学领域:
- 河流工程是河流工程.
- 液压结构的水力结构.
- 计算流体动力学 计算流体动力学
背景情况:
- 在堤附近的冲威胁到河岸的稳定性.
- 对于河流工程来说,准确地预测最大冲洗深度至关重要.
- 冲堤周围的冲现象复杂且难以建模.
研究的目的:
- 开发一种可靠的集体数据驱动模型,用于预测围绕堤的扫地深度.
- 将随机树 (RT) 与增量回归 (AR),包装 (B) 和随机子空间 (RSS) 进行混合,以提高预测准确度.
- 为了确定影响扫地深度预测的最有影响力的参数.
主要方法:
- 从现有文献中收集了154个实验观测数据库.
- 进行无维分析,选择四个输入变量 (v/vs, y/l, l/d50, Fd50) 和一个响应变量 (ds/l).
- 开发和比较混合模型,包括包装-添加回归-随机树 (B-AR-RT),用于扫地深度预测.
主要成果:
- B-AR-RT模型实现了0.9693.3的高确定系数 (R2)
- 该模型显示了0.1305.5的低根平均平方误差 (RMSE).
- 纳什-萨特克利夫效率 (NSE) 达到0.9692,表明了出色的性能.
结论:
- 开发的B-AR-RT模型提供了一种可靠的方法,用于预测围绕堤的扫地深度.
- 与以前的方法相比,混合组合方法显著提高了预测准确性.
- 灵敏度分析确定了影响扫深度的关键参数,有助于工程设计.
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