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使用TRMM降雨数据对混合机器学习算法的评估,用于预测巴西东部特雷斯马里亚斯水库 (Tres Marias Reservoir) 的每日流入量

Ehab Gomaa¹, Bilel Zerouali², Salah Difi²

¹Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia.

|August 18, 2023

概括

由粒子群优化 (MLP-PSO) 优化的多层感知子模型在预测流水放电方面表现出色,优于高斯过程回归 (GPR) 和高斯辐射基函数神经网络 (GRNN) 模型. 将MLP-PSO与实证模式分解-希尔伯特-黄变换 (EMD-HHT) 结合起来,进一步提高了流量预测的准确性.

科学领域:

水文和水资源工程水文和水资源工程
环境科学中的人工智能
计算流体动力学的流体动力学.

背景情况:

准确的降雨-流水建模对于水资源管理和洪水预测至关重要.
传统的水文模型往往难以应对降雨流失过程的复杂,非线性动态.
先进的机器学习技术为改善水文预测提供了潜力.

研究的目的:

评估和比较高斯辐射基函数神经网络 (GRNN),高斯过程回归 (GPR) 和通过粒子群优化 (MLP-PSO) 优化的多层感知子的性能,用于降雨-流水分析.
研究将实证模式分解-希尔伯特-黄变换 (EMD-HHT) 与GPR和MLP-PSO集成用于流量预测的有效性.
为了确定最佳的模型配置,以准确地预测流水排放.

主要方法:

应用GRNN,GPR和MLP-PSO模型,使用自回归输入向量从TRMM降雨和TMR流入数据中获得.
使用统计指标进行绩效评估,包括根平均平方误差 (RMSE).
将经验模式分解-希尔伯特-黄变换 (EMD-HHT) 与GPR和MLP-PSO模型集成,以提高流量预测.

主要成果:

MLP-PSO模型始终表现出优于GRNN和GPR的性能,在各种输入组合中实现了最低的RMSE.
与GPR-EMD模型相比,MLP-PSO-EMD模型在流量预测方面表现出更高的准确性.
模型M4,简化的MLP-PSO-EMD配置,产生了最有利的结果与最低的RMSE值.

结论:

该MLP-PSO模型对于降雨流水分析和流水排放预测非常有效.
集成EMD-HHT显著提高了水文模型的预测准确度,特别是与MLP-PSO.
这些发现为在水文研究中选择和实施先进的计算模型提供了宝贵的指导.

关键词:

巴西河流流域的流域经验式模式分解 (EMD)洪水洪水洪水洪水水文模拟水文模拟粒子群集优化 (PSO) 是一种水资源管理水资源的管理.

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