Abstract
Monitoring and quantifying suspended sediment concentration (SSC) along major fluvial systems such as the Missouri and Mississippi Rivers provide crucial information for biological processes, hydraulic infrastructure, and navigation. Traditional monitoring based on in situ measurements lack the spatial coverage necessary for detailed analysis. This study developed a method for quantifying SSC based on Landsat imagery and corresponding SSC data obtained from United States Geological Survey monitoring stations from 1982 to present. The presented methodology first uses feature fusion based on canonical correlation analysis to extract pertinent spectral information, and then trains a predictive reflectance–SSC model using a feed-forward neural network (FFNN), a cascade forward neural network (CFNN), and an extreme learning machine (ELM). The trained models are then used to predict SSC along the Missouri–Mississippi River system. Results demonstrated that the ELM-based technique generated R2 > 0.9 for Landsat 4–5, Landsat 7, and Landsat 8 sensors and accurately predicted both relatively high and low SSC displaying little to no overfitting. The ELM model was then applied to Landsat images producing quantitative SSC maps. This study demonstrates the benefit of ELM over traditional modeling methods for the prediction of SSC based on satellite data and its potential to improve sediment transport and monitoring along large fluvial systems.
Highlights
The measuring and monitoring of suspended sediment concentration (SSC) and its transport is critical to understanding the fluvial dynamics of large streams and rivers such as the Missouri and Mississippi Rivers in the United States
The SSC data provided by the United States Geological Survey (USGS) was cross-sectional daily mean SSC reported in milligrams per liter, which is computed by the USGS using regression models that relate daily direct point measurements to monthly cross-sectional SSC measurements [28]
extreme learning machine (ELM) generated the highest R2 and lowest root mean square error (RMSE) values in all cases when applied to the testing data
Summary
The measuring and monitoring of suspended sediment concentration (SSC) and its transport is critical to understanding the fluvial dynamics of large streams and rivers such as the Missouri and Mississippi Rivers in the United States. Excess sediment loads are the most common issue related to sediment transport rate and have been found to cause issues such as impaired navigation from channel aggradation, harm dams and water intake infrastructure, reduced photosynthesis and dissolved oxygen levels, and harmful algae blooms due to the transport of excess inorganic nutrients [2,3]. Issues can arise related to a lack of sediment transport, or sediment starvation, such as the reduction of ecological habitat and even threaten hydraulic infrastructure (e.g., flood levees, piers, and jetties) as a result of erosion from structures like dams [4,5]. The monitoring and evaluation of SSC is essential in determining water quality and associated hydrologic functions
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