A New Machine-Learning Approach for Classifying Hysteresis in Suspended-Sediment Discharge Relationships Using High-Frequency Monitoring Data

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.