机器学习方法用于充分预测带有床形的流水道中的流阻
Ajaz Ahmad Mir1, Mahesh Patel2
1Dr B R Ambedkar National Institute of Technology Jalandhar, Jalandhar-144008, Punjab, India
概括
机器学习模型准确地预测了曼宁.
科学领域:
- 水文学和流体力学 流体力学
- 计算科学 计算科学
背景情况:
- 河流流动力学是由流动阻力和床形粗度所支配的.
- 预测曼宁粗度系数 (n) 对液压工程至关重要.
- 现有的预测"n"的方法在带床形的流道中需要改进.
研究的目的:
- 开发和评估用于预测曼宁粗度系数 (n) 的机器学习 (ML) 模型.
- 用统计指标和交叉验证来评估各种ML模型的性能.
- 确定影响流河流粗度预测的关键参数.
主要方法:
- 利用六个输入特征来训练ML模型.
- 采用随机森林,额外树木回归,极端梯度提升和拉索回归.
- 使用皮尔森的R平方,灵敏度分析,泰勒图和K折交叉验证来评估模型性能.
主要成果:
- 随机森林,额外树木回归和极端梯度提升实现了高精度 (R平方≥0.99).
- 拉索回归证明了适度的预测效率.
- 灵敏度分析显示,能量等级线是粗度的重要预测因素.
结论:
- 机器学习模型提供了一个可靠的替代方案,用于预测流道中曼宁粗度系数.
- 这项研究增强了对河床特征和粗度之间的复杂关系的理解.
- 使用ML进行准确的'n'预测可以改善液压建模和河流管理.
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