Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2017WR022238 |
A New Machine-Learning Approach for Classifying Hysteresis in Suspended-Sediment Discharge Relationships Using High-Frequency Monitoring Data | |
Hamshaw, Scott D.1,2; Dewoolkar, Mandar M.1; Schroth, Andrew W.3; Wemple, Beverley C.4; Rizzo, Donna M.1 | |
2018-06-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2018 |
卷号 | 54期号:6页码:4040-4058 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Studying the hysteretic relationships embedded in high-frequency suspended-sediment concentration and river discharge data over 600(+) storm events provides insight into the drivers and sources of riverine sediment during storm events. However, the literature to date remains limited to a simple visual classification system (linear, clockwise, counter-clockwise, and figure-eight patterns) or the collapse of hysteresis patterns to an index. This study leverages 3 years of suspended-sediment and discharge data to show proof-of-concept for automating the classification and assessment of event sediment dynamics using machine learning. Across all catchment sites, 600(+) storm events were captured and classified into 14 hysteresis patterns. Event classification was automated using a restricted Boltzmann machine (RBM), a type of artificial neural network, trained on 2-D images of the suspended-sediment discharge (hysteresis) plots. Expansion of the hysteresis patterns to 14 classes allowed for new insight into drivers of the sediment-discharge event dynamics including spatial scale, antecedent conditions, hydrology, and rainfall. The probabilistic RBM correctly classified hysteresis patterns (to the exact class or next most similar class) 70% of the time. With increased availability of high-frequency sensor data, this approach can be used to inform watershed management efforts to identify sediment sources and reduce fine sediment export. |
英文关键词 | suspended sediment hysteresis concentration-discharge relationships pattern recognition restricted Boltzmann machine event sediment dynamics |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000440309900016 |
WOS关键词 | HEADWATER CATCHMENT ; NEURAL-NETWORKS ; STORM EVENTS ; DYNAMICS ; RIVER ; VARIABLES ; PATTERNS ; NUTRIENT ; BASINS ; RUNOFF |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/21524 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Vermont, Coll Engn & Math Sci, Dept Civil & Environm Engn, Burlington, VT 05405 USA; 2.Univ Vermont, Vermont EPSCoR, Burlington, VT 05405 USA; 3.Univ Vermont, Coll Arts & Sci, Dept Geol, Burlington, VT USA; 4.Univ Vermont, Coll Arts & Sci, Dept Geog, Burlington, VT USA |
推荐引用方式 GB/T 7714 | Hamshaw, Scott D.,Dewoolkar, Mandar M.,Schroth, Andrew W.,et al. A New Machine-Learning Approach for Classifying Hysteresis in Suspended-Sediment Discharge Relationships Using High-Frequency Monitoring Data[J]. WATER RESOURCES RESEARCH,2018,54(6):4040-4058. |
APA | Hamshaw, Scott D.,Dewoolkar, Mandar M.,Schroth, Andrew W.,Wemple, Beverley C.,&Rizzo, Donna M..(2018).A New Machine-Learning Approach for Classifying Hysteresis in Suspended-Sediment Discharge Relationships Using High-Frequency Monitoring Data.WATER RESOURCES RESEARCH,54(6),4040-4058. |
MLA | Hamshaw, Scott D.,et al."A New Machine-Learning Approach for Classifying Hysteresis in Suspended-Sediment Discharge Relationships Using High-Frequency Monitoring Data".WATER RESOURCES RESEARCH 54.6(2018):4040-4058. |
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