基于经验小波变换和变分模态分解的机器学习技术在泥沙浓度估计中的应用

IF 4.1 3区地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY

Physics and Chemistry of the Earth Pub Date : 2025-10-01 DOI:10.1016/j.pce.2025.104136

Hatice Citakoglu , Oguz Simsek , Pınar Spor , Yunus Emre Gun

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引用次数: 0

摘要

本研究比较了最小二乘支持向量回归（LS-SVM）、广义加性模型（GAM）、多元自适应样条回归（MARS）、基于经验小波变换（EWT）的高斯过程回归（GPR）和变分模态分解（VMD）预处理技术在幼发拉底河流域河流站沉积物浓度估算中的性能。研究中表现最好的模型为2102 （Murat River-Palu）的VMD-GPR (R2 = 0.994, RRMSE = 12.70, NSE = 0.993)；VMD-MARS为2115 (Göksu River-Malpınar) (R2 = 0.968, RRMSE = 45.99, NSE = 0.961)；EWT-MARS方法预测的2119（幼发拉底河-凯马海峡）（R2 = 0.998, RRMSE = 6.16, NSE = 0.998）；VMD-GPR检测结果为2133 (Munzur stream - melekbahpere) (R2 = 0.976, RRMSE = 28.67, NSE = 0.973)；2164 (Goynuk Stream-Cayağzı)VMD-GPR (R2 = 0.982,推定= 25.86,分析了无= 0.980);对于2166 (Peri Suyu-Loğmar) EWT-MARS (R2 = 0.993, RRMSE = 13.83, NSE = 0.993)，对于2176 (Tacik Deresi-Mutu) EWT-GPR （R2 = 0.990, RRMSE = 17.48）。根据性能标准，VMD和EWT与GPR和MARS机器学习方法更兼容。这些结果表明，机器学习和人工智能技术为沉积物迁移估计提供了强有力的替代方案。VMD-GPR（三站）模式和EWT-MARS模式错误率低、精度高。本研究的发现为大坝管理、水工结构工程和基于流域的侵蚀分析提供了关键数据，并对环境和水文模型研究做出了重大贡献。

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Application of machine learning techniques with empirical wavelet transform and variational mode decomposition for sediment concentration estimation

This study compares the performances of Least-Square Support Vector Regression (LS-SVM), Generalized Additive Model (GAM), Multivariate Adaptive Regression Splines (MARS), Gaussian Process Regression (GPR) methods supported by Empirical Wavelet Transform (EWT) and Variational Mode Decomposition (VMD) preprocessing techniques for estimating sediment concentration at river stations in the Euphrates Basin. In the study, the best performing models are the VMD-GPR (R² = 0.994, RRMSE = 12.70; NSE = 0.993) for 2102 (Murat River-Palu); 2115 (Göksu River-Malpınar) for VMD-MARS (R² = 0.968, RRMSE = 45.99, NSE = 0.961); 2119 (Euphrates River-Kemah Strait) for EWT-MARS method (R² = 0.998, RRMSE = 6.16, NSE = 0.998); 2133 (Munzur Stream-Melekbahçe) for VMD-GPR (R² = 0.976, RRMSE = 28.67, NSE = 0.973); 2164 (Göynük Stream-Çayağzı) for VMD-GPR (R² = 0.982, RRMSE = 25.86, NSE = 0.980); For 2166 (Peri Suyu-Loğmar) EWT-MARS (R² = 0.993, RRMSE = 13.83, NSE = 0.993), for 2176 (Tacik Deresi-Mutu) EWT-GPR (R² = 0.990, RRMSE = 17.48). According to performance criteria, VMD and EWT were more compatible with GPR and MARS machine learning methods. These results reveal that machine learning and artificial intelligence techniques offer a strong alternative for sediment transport estimation. It was observed that the VMD-GPR (three stations) and EWT-MARS models stood out with their low error rates and high accuracy levels. This study's findings provide critical data regarding dam management, hydraulic structures engineering, and basin-based erosion analyses, and they significantly contribute to environmental and hydrological modelling studies.

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来源期刊

Physics and Chemistry of the Earth 地学-地球科学综合

CiteScore

5.40

自引率

2.70%

发文量

176

审稿时长

31.6 weeks

期刊介绍： Physics and Chemistry of the Earth is an international interdisciplinary journal for the rapid publication of collections of refereed communications in separate thematic issues, either stemming from scientific meetings, or, especially compiled for the occasion. There is no restriction on the length of articles published in the journal. Physics and Chemistry of the Earth incorporates the separate Parts A, B and C which existed until the end of 2001. Please note: the Editors are unable to consider submissions that are not invited or linked to a thematic issue. Please do not submit unsolicited papers. The journal covers the following subject areas: -Solid Earth and Geodesy: (geology, geochemistry, tectonophysics, seismology, volcanology, palaeomagnetism and rock magnetism, electromagnetism and potential fields, marine and environmental geosciences as well as geodesy). -Hydrology, Oceans and Atmosphere: (hydrology and water resources research, engineering and management, oceanography and oceanic chemistry, shelf, sea, lake and river sciences, meteorology and atmospheric sciences incl. chemistry as well as climatology and glaciology). -Solar-Terrestrial and Planetary Science: (solar, heliospheric and solar-planetary sciences, geology, geophysics and atmospheric sciences of planets, satellites and small bodies as well as cosmochemistry and exobiology).