在清水条件下预测最大冲洗深度:在经验方程和机器学习方法之间进行多变量和强大的比较分析,使用广泛的参考元数据
Buddhadev Nandi1, Gaurav Patel1, Subhasish Das1
1School of Water Resources Engineering, Jadavpur University, Kolkata 700032, India.
Journal of environmental management
|February 24, 2024
概括
桥梁码头扫描,对结构稳定性的风险,使用先进的机器学习组合进行了分析. 随机梯度提升 (SGB) 和包装回归树 (BRT) 模型表现出最高扫描深度的卓越预测准确性.
科学领域:
- 土木工程 土木工程是指土木工程.
- 液压工程 液压工程 液压工程
- 计算科学 计算科学
背景情况:
- 在桥梁码头周围的局部清理增加了沉积物运输和结构故障的风险.
- 扫描深度评估传统上依赖于液压关系或数据驱动软计算模型.
- 现有的方法在准确性和物理解释方面存在局限性.
研究的目的:
- 开发和评估创新的集体机器学习框架,用于预测清水条件下的最大扫描深度 (dsm).
- 为了比较这些新合奏的表现与既定的经验方程.
- 确定影响扫地深度预测的关键变量.
主要方法:
- 使用支持向量回归机器 (SVMR),随机森林回归 (RFR) 和减少错误修剪树 (REPTree) 作为基础学习者创建了整体框架.
- 包装回归树 (BRT) 和随机梯度提升 (SGB) 被用作元学习器.
- 这些模型在35个来自63年的文献的实验数据集上进行了训练和测试,使用统计指标评估性能.
主要成果:
- 整体模型,特别是SGB (REPTree) 和BRT (SVMR-PUK),在预测最大扫描深度方面表现优于六个顶级实证方程.
- 纳迪和达斯 (2023) 的实证方程在传统方法中表现最好.
- 灵敏度分析显示,沉积物分级和流量强度是对冲深度最有影响力的预测因素.
结论:
- 与实证方法相比,先进的集体机器学习模型为预测桥梁码头扫描深度提供了更高的准确性.
- 开发的SGB和BRT集成为评估液压工程中的扫地风险提供了强大的工具.
- 了解沉积物特征和流量条件的影响对于准确的扫预测至关重要.
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