Xuan Zhang , Jian Luo , Ruihong Yu , Ping Miao , Lanxuan Yin
{"title":"基于统计和机器学习的黄河流域典型粗沙区输沙预测","authors":"Xuan Zhang , Jian Luo , Ruihong Yu , Ping Miao , Lanxuan Yin","doi":"10.1016/j.ejrh.2025.102777","DOIUrl":null,"url":null,"abstract":"<div><h3>Study region</h3><div>Inner Mongolia Autonomous Region (China).</div></div><div><h3>Study focus</h3><div>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.</div></div><div><h3>New hydrogeological insights from the region</h3><div>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.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"62 ","pages":"Article 102777"},"PeriodicalIF":5.0000,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrating statistical and machine learning approaches for sediment transport prediction in a typical coarse sandy region of the Yellow River Basin\",\"authors\":\"Xuan Zhang , Jian Luo , Ruihong Yu , Ping Miao , Lanxuan Yin\",\"doi\":\"10.1016/j.ejrh.2025.102777\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Study region</h3><div>Inner Mongolia Autonomous Region (China).</div></div><div><h3>Study focus</h3><div>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.</div></div><div><h3>New hydrogeological insights from the region</h3><div>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.</div></div>\",\"PeriodicalId\":48620,\"journal\":{\"name\":\"Journal of Hydrology-Regional Studies\",\"volume\":\"62 \",\"pages\":\"Article 102777\"},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2025-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Hydrology-Regional Studies\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214581825006068\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"WATER RESOURCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hydrology-Regional Studies","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214581825006068","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"WATER RESOURCES","Score":null,"Total":0}
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.
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.