Calculation of Suspended Sediment Concentration Based on Deep Learning and OBS Turbidity

Temporal and Spatial Distribution of Suspended Sediment Concentration in Lakes Based on Satellite Remote Sensing and Internet of Things

“…With the help of remote sensing technology, the information on the concentration of suspended sediment that traditional methods require a lot of work can be obtained faster, better, and more easily. Water color remote sensing is based on the spectral characteristics of water absorption and scattering in visible light or near-infrared, using the water spectral radiation data measured by aviation and aerospace sensors to interpret the relevant phenomena and parameters of the water body [3].…”

A physics-informed statistical learning framework for forecasting local suspended sediment concentrations in marine environment

“…But studies on the forecasting of SSC with DDMs in oceans are still limited (Oehler et al, 2012;Zhang et al, 2021c), although many studies have been conducted for SSC prediction in rivers and DDMs have generally shown better performance than process-based models in terms of accuracy (Jaffe and Rubin, 1996;Nagy et al, 2002;Bhattacharya et al, 2007;Kişi, 2010;Zounemat-Kermani et al, 2016;Buyukyildiz and Kumcu, 2017;Malik et al, 2019). In these works, complex patterns between the SSC and relevant environmental factors are extracted through either machine learning approaches (Cigizoglu, 2004), deep learning with more hidden layers (Hamshaw et al, 2018;Ying et al, 2020) or statistical modelling approaches (Kuhnert et al, 2012). Machine learning approaches mainly approximate a mapping for SSC pattern partitioning through a combination of nonlinear transformations or kernels, such as artificial neural networks (Kabiri-Samani et al, 2011;Khan et al, 2019;James et al, 2018;Teixeira et al, 2020), support vector machines (Kişi, 2012), and tree regression (Malik et al, 2017;Huang et al, 2021), and have achieved better prediction accuracy than physical models, but the physical interpretation is relatively limited.…”