基于统计和机器学习的黄河流域典型粗沙区输沙预测

IF 5 2区 地球科学 Q1 WATER RESOURCES
Xuan Zhang , Jian Luo , Ruihong Yu , Ping Miao , Lanxuan Yin
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引用次数: 0

摘要

研究区域:内蒙古自治区(中国)。研究了2007 - 2021年十支流地区径流、降水、潜在蒸散(PET)和归一化植被指数(NDVI)与泥沙负荷的多尺度相关性。此外,利用统计模型和机器学习(ML)技术预测泥沙输运,以增强对不同环境条件下泥沙动力学的理解。本研究为黄河粗沙区泥沙负荷的尺度控制定量研究提供了新思路。采用多元经验模态分解(MEMD)方法,将泥沙荷载及其相关变量的原始时间序列分解为5个或6个本征模态函数(IMFs)和1个残差分量。时间相关分析表明,泥沙负荷与环境因子的关系具有动态的、多尺度的特征。径流量是影响毛布拉和溪柳沟流域输沙量的关键因素。从IMF1到IMF5,径流主导输沙过程,而在IMF6, PET主导输沙过程。采用多层感知器(MLP)、卷积神经网络(CNN)和粒子群优化-支持向量回归(PSO-SVR) 3种机器学习模型对不同流域的泥沙负荷进行预测。将MEMD与ML相结合,可显著提高泥沙负荷预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating statistical and machine learning approaches for sediment transport prediction in a typical coarse sandy region of the Yellow River Basin

Study region

Inner Mongolia Autonomous Region (China).

Study focus

This study investigated the multiscale correlations among runoff, precipitation, potential evapotranspiration (PET), and normalized difference vegetation index (NDVI) with sediment load in the Ten Tributaries region from 2007 to 2021. Furthermore, sediment transport was predicted using statistical models and machine learning (ML) techniques to enhance understanding of sediment dynamics under varying environmental conditions.

New hydrogeological insights from the region

This work provided novel insights on the quantification of the scale-specific controls of sediment load in the coarse sandy region of the Yellow River. Multivariate empirical mode decomposition (MEMD) was employed to decompose the original time series of sediment load and its associated variables into five or six intrinsic mode functions (IMFs) and one residual component. Time-dependent intrinsic correlation (TDIC) analysis revealed that the relationships between sediment load and environmental factors exhibit dynamic, multi-scale properties. Runoff was the key factor affecting sediment load in Maobula and Xiliugou watershed. In Hantaichuan watershed, runoff dominated sediment load dynamics from IMF1 to IMF5, whereas PET governed the sediment transport process at IMF6. Three machine learning models, multilayer perceptron (MLP), convolutional neural networks (CNN) and particle swarm optimization-support vector regression (PSO-SVR) were applied to forecast sediment load in different basins. Integrating MEMD with ML significantly enhanced sediment load prediction accuracy.
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来源期刊
Journal of Hydrology-Regional Studies
Journal of Hydrology-Regional Studies Earth and Planetary Sciences-Earth and Planetary Sciences (miscellaneous)
CiteScore
6.70
自引率
8.50%
发文量
284
审稿时长
60 days
期刊介绍: Journal of Hydrology: Regional Studies publishes original research papers enhancing the science of hydrology and aiming at region-specific problems, past and future conditions, analysis, review and solutions. The journal particularly welcomes research papers that deliver new insights into region-specific hydrological processes and responses to changing conditions, as well as contributions that incorporate interdisciplinarity and translational science.
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