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Full-Text Articles in Hydrology
Predicting River Stage Using Recurrent Neural Networks, Eric Rohli
Predicting River Stage Using Recurrent Neural Networks, Eric Rohli
LSU Master's Theses
River stage prediction is an important problem in the water transportation industry. Accurate river stage predictions provide crucial information to barge and tow boat operators, port terminal captains, and lock management officials. Shallow river levels caused by prolonged drought impact the loading capacity of barges and tow boats. High river levels caused by excessive rainfall or snowmelt allow for greater tow capacities but make downstream transportation and lock management risky. Current academic river height prediction systems utilize either time series statistical analysis or machine learning algorithms to forecast future river heights, but systems that combine these two areas often limit …
Fluvial Processes In Motion: Measuring Bank Erosion And Suspended Sediment Flux Using Advanced Geomatic Methods And Machine Learning, Scott Douglas Hamshaw
Fluvial Processes In Motion: Measuring Bank Erosion And Suspended Sediment Flux Using Advanced Geomatic Methods And Machine Learning, Scott Douglas Hamshaw
Graduate College Dissertations and Theses
Excessive erosion and fine sediment delivery to river corridors and receiving waters degrade aquatic habitat, add to nutrient loading, and impact infrastructure. Understanding the sources and movement of sediment within watersheds is critical for assessing ecosystem health and developing management plans to protect natural and human systems. As our changing climate continues to cause shifts in hydrological regimes (e.g., increased precipitation and streamflow in the northeast U.S.), the development of tools to better understand sediment dynamics takes on even greater importance. In this research, advanced geomatics and machine learning are applied to improve the (1) monitoring of streambank erosion, (2) …