Geologic Publications for Mount Rainier

A new machine‐learning approach for classifying hysteresis in suspended‐sediment discharge relationships using high‐frequency monitoring data

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Author(s): Scott D. Hamshaw, Mandar M. Dewoolkar, Andrew W. Schroth, Beverley C. Wemple, Donna M. Rizzo

Category: PUBLICATION
Document Type:
Publisher: Water Resources Research
Published Year: 2018
Volume: 54
Number: 6
Pages: 4040 to 4058
DOI Identifier: 10.1029/2017WR022238
ISBN Identifier:
Keywords: suspended sediment hysteresis concentration‐discharge relationships pattern recognition restricted Boltzmann machine event sediment dynamics

Abstract:
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.

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Suggested Citations:
In Text Citation:
Hamshaw and others (2018) or (Hamshaw et al., 2018)

References Citation:
Hamshaw, S.D., M.M. Dewoolkar, A.W. Schroth, B.C. Wemple, and D.M. Rizzo, 2018, A new machine‐learning approach for classifying hysteresis in suspended‐sediment discharge relationships using high‐frequency monitoring data: Water Resources Research, Vol. 54, No. 6, pp. 4040-4058, doi: 10.1029/2017WR022238 .