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


TitleA new machine-learning approach for classifying hysteresis in suspended-sediment discharge relationships using high-frequency monitoring data
Publication TypeJournal Article
Year of Publication2018
AuthorsHamshaw, SD, Dewoolkar, MM, Schroth, AW, Wemple, B, Rizzo, DM
JournalWater Resources Research
Volume54
Start Page4040
Issue6
Pagination4040-4058
Date Published2018/05
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 three 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.

URLhttps://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2017WR022238
DOI10.1029/2017WR022238
Refereed DesignationRefereed
Status: 
Published
Attributable Grant: 
BREE
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Year3 StatusChanged
Acknowledged VT EPSCoR: 
Ack-Yes
2nd Attributable Grant: 
RACC
2nd Grant Year: 
2nd_Post_Grant
2nd Acknowledged Grant: 
2nd_Ack-Yes