使用机器学习算法在垂直断水上进行增强的波浪超越模拟
M A Habib1, J J O'Sullivan1,2, S Abolfathi3
1UCD Dooge Centre for Water Resources Research, School of Civil Engineering, University College Dublin, Dublin, Ireland.
PloS one
|August 16, 2023
概括
机器学习模型准确地预测了垂直海墙上的波浪覆盖. 随机森林和梯度增强决策树为沿海保护战略提供了卓越的性能和计算效率.
科学领域:
- 沿海工程的工程.
- 液压系统 液压系统 液压系统
- 机器学习应用程序 机器学习应用程序
背景情况:
- 准确的波浪超标预测对于防范海平面上升和风暴浪潮的沿海防御至关重要.
- 传统方法依赖于物理和数值模型的经验关系.
- 机器学习 (ML) 为超越预测提供了先进的数据驱动方法.
研究的目的:
- 评估四种ML技术的性能,用于预测垂直海墙上的波浪突破排放.
- 为了比较随机森林,梯度增强决策树,支持向量机-回归和人工神经网络的预测准确度和计算效率.
- 调查ML在各种沿海条件下对波浪超标评估的适用性.
主要方法:
- 使用EurOtop (2018) 数据库开发ML模型.
- 应用超参数调整用于算法优化.
- 使用特征转换和选择来减少数据冗余并防止过度拟合.
- 统计分析以比较随机森林,梯度增强决策树,支持向量机-回归和人工神经网络的性能.
主要成果:
- 随机森林展示了卓越的预测性能,其次是梯度增强决策树,支持向量机-回归和人工神经网络.
- 基于决策树的方法 (随机森林,梯度增强决策树) 在计算上比支持向量机-回归和人工神经网络更有效.
- 渐变增强决策树表现出最快的模拟时间.
结论:
- 机器学习方法提供了一种可靠且有效的计算方法,用于在垂直海墙上评估波浪覆盖.
- 随机森林和梯度增强决策树因其在沿海工程应用中的准确性和效率而被推.
- 机器学习模型可以有效地评估波浪覆盖在广泛的水力动力学和结构条件.
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