极端学习机器的综合方法和局部加权线性回归用于改进放电系数预测
Mohammed Majeed Hameed1, Mohamed Khalid Alomar2, Siti Fatin Mohd Razali3,4
1Upper Euphrates Centre for Sustainable Development Research, University of Anbar, Ramadi City, 31001, Iraq. mohmmag1@gmail.com.
Scientific reports
|July 2, 2025
概括
本研究引入了一种增强的极端学习机器模型 (ELM-LWLR),用于在侧中准确预测放电系数 (Cd). 新型号显著提高了精度,为液压工程提供了强大的工具.
科学领域:
- 水力学和流体力学 流体力学
- 计算智能是一种计算智能.
- 水资源工程 水资源工程
背景情况:
- 准确的排放系数 (Cd) 确定对于侧堤流量计算至关重要.
- 现有的极端学习机器 (ELM) 模型由于其线性输出层而存在泛化的局限性.
研究的目的:
- 为了提高排放系数 (Cd) 的预测准确度,用于矩形尖顶侧.
- 通过整合局部加权线性回归 (LWLR) 来克服ELM模型的概括限制.
主要方法:
- 开发了一个混合极端学习机器-局部加权线性回归 (ELM-LWLR) 模型.
- 在LWLR中利用一个辐射基础内核函数来捕获非线性关系.
- 验证了ELM-LWLR模型与多重线性回归 (MLR),ELM,LWLR和极端梯度增强 (XGBoost) 相对应.
主要成果:
- ELM-LWLR模型表现出卓越的性能,相关系数为0.968和PBIAS为-0.130%.
- 实现了显著的精度改进:比LWLR提高37.21%,比XGBoost提高28.95%,比ELM提高48.08%,比MLR提高64.94%.
- 灵敏度分析确定了的高度与长度比和无维长度作为影响Cd的关键因素.
结论:
- ELM-LWLR模型是用于侧堤排放系数建模的实用和强大的工具.
- 这种方法在复杂的工程应用中为降低成本和增强液压建模提供了显著的优势.
- 该研究强调了将ELM与LWLR相结合的有效性,以改善液压工程中的非线性数据模式识别.
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