LXGB:一种机器学习算法,用于估计伪共迷宫的排放系数
Somayeh Emami1, Hojjat Emami2, Javad Parsa3
1Department of Water Engineering, University of Tabriz, Tabriz, 5971982284, Iran. somayehemami70@gmail.com.
Scientific reports
|July 29, 2023
概括
一个新的伪线迷宫 (PCLW) 设计提高了效率. 混合机器学习算法准确估计其放电系数,优于其他方法.
科学领域:
- 液压工程 液压工程 液压工程
- 计算流体动力学的流体动力学.
- 水资源管理 水资源管理
背景情况:
- 堤防效率通常通过修改平面几何和增加堤防长度来提高.
- 排放系数 (Cd) 是堤性能的一个关键参数.
- 传统方法可能无法完全捕捉复杂的液压行为.
研究的目的:
- 介绍一个新的伪线迷宫 (PCLW) 设计.
- 开发和评估混合机器学习算法 (LXGB),用于估计PCLW的放电系数 (Cd).
- 确定最佳的输入参数,以准确估计Cd.
主要方法:
- 采用了混合LXGB算法,将LSHADE和XGBoost结合起来.
- 通过使用PCLW1和PCLW2模型的132个数据序列,测试了7个输入场景.
- 绩效使用RMSE,RRMSE和NSE指标进行评估.
主要成果:
- 该LXGB模型实现了高精度,平均RMSE = 0.009,RRMSE = 0.010,和NSE = 0.977.
- 最有效的输入参数是R/W,L/W和H/W.
- 与SAELM,ANFIS-FFA,GEP和ANN相比,LXGB模型表现出更高的性能.
结论:
- 拟议的PCLW设计为提高水效率提供了一个实际的解决方案.
- 混合智能方法LXGB对于估计PCLW的排放系数非常有效.
- 几何和液压比率是排放系数估计的关键预测指标.
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