水利工程的人工智能:预测梯形侧堤的排放系数
Mehdi Fuladipanah1, Saleema Panda2, Namal Rathnayake3
1Department of Civil Engineering, Ramh. C., Islamic Azad University, Ramhormoz, Iran.
Mathematical biosciences and engineering : MBE
|December 2, 2025
概括
本研究介绍了一种人工智能 (AI) 框架,用于预测侧堤的排放系数 (Cd). 人工神经网络 (ANN) 与其他机器学习模型相比,显示出更高的准确性.
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:
- 准确预测排放系数 (Cd) 对侧的液压设计和性能至关重要.
- 传统的Cd预测方法对于复杂的几何形状 (如梯形迷宫侧) 的准确性可能受到限制.
研究的目的:
- 开发和比较人工智能 (AI) 和机器学习模型 (MLM) 以提高在双循环梯形迷宫侧中Cd的预测.
- 确定影响Cd预测的最有影响力的液压和几何参数.
主要方法:
- 使用实验室数据集开发了三种MLM (支持矢量机器,人工神经网络,基因表达编程).
- 使用 Γ 测试进行的灵敏度分析确定了五个关键输入参数:Fr,L/B,Le/L, (Y1-P) /P 和 α.
- 在训练,测试和验证阶段使用RMSE,MAE,R2和Cd (DDRmax) 评估模型性能.
主要成果:
- 所有三个开发的MLM都在预测Cd方面表现得很好.
- 人工神经网络 (ANN) 模型,特别是MLP5-7-1架构,表现出最高的预测准确性和稳定性.
- 该ANN模型取得了优异的验证结果,其中RMSE=0.0061,MAE=0.0003,R2=0.9301,Cd(DDRmax) =5.22.
结论:
- 机器学习模型,特别是ANN,为预测复杂液压结构中的Cd提供了精确有效的方法.
- 该研究验证了AI在推进液压工程设计和分析方面的能力.
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