Discussion. Design Curves for Suspended Load Estimation.

Fleming, George; Harrison, A P; Fleming, Jh; Rees, Karen; Kite, G. W.; Chitale, S.V.; Herbertson, Jg; Mb, Collins

doi:10.1680/iicep.1970.6984

Cited by 2 publications

(2 citation statements)

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“…Streamflow has been identified as the primary explanatory variable for SSC although it is not always directly related to SSC and that the relationship between the two is known to vary greatly [4][5][6]. The complexity of the sediment concentration transported hydrological phenomenon, which is a result of a number of ambiguous parameters including spatial variability of basin characteristics, river discharge patterns, and inherent non-linearity in hydro-meteorological parameters, is what primarily drives this variation [7]. Other factors to take into account are the availability of sediment, seasonality, and the location where the source resides within the watershed [8].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Modeling of Daily Suspended Sediment Concentration and Discharge in Esopus Creek

Khosravi¹,

Ghoochani²,

Nazemi³

2023

Preprint

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The intrinsic non-linearity and complexity of suspended sediment dynamics, which are impacted by the geographical variability of basin parameters and temporal climatic patterns, make it difficult to estimate suspended sediment concentration (SSC) accurately in hydrological processes. Deep neural networks (DNNs), a cutting-edge modeling method that can capture the innate non-linearity in hydrological systems, have emerged as a solution to this problem. Using primary data on discharge, SSC, and turbidity, the long short-term memory method of DNNs was employed in this study to simulate the discharge-suspended sediment connection for the Esopus Creek, NY, USA as the first objective. To develop effective modeling strategies, multiple scenarios of feature selection, including combinations of discharge, turbidity and SSC for preceding days, were considered. Secondly, the variations in SSC and discharge between the three USGS gages along Esopus Creek that were upper stream, lower stream, and downstream, adjacent to the pourpoint were analyzed. Statistical metrics and scenarios of feature importance were used to evaluate the effectiveness of the DNN-based models. The study shows that among 24 DNN approaches feature selection is crucial in simulating the daily SSC, and discharge and presenting novel research directions for utilizing machine learning algorithms in water quality.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Modeling of Daily Suspended Sediment Concentration and Discharge in Esopus Creek

Khosravi¹,

Ghoochani²,

Nazemi³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…to SSC and that the relationship between the two is known to vary greatly [4][5][6]. As a result, this variation is caused primarily by the complexity of the sediment concentration transported hydrological phenomenon as a result of several ambiguous parameters such as spatial variability of basin characteristics, river discharge patterns, and inherent nonlinearity in hydro-meteorological parameters [7]. Other considerations include sediment availability, seasonality, and the location of the source within the watershed [8].…”

Section: Introductionmentioning

confidence: 99%

Demystifying the Relationship Between River Discharge and Suspended Sediment Using Exploratory Analysis and Deep Neural Network Algorithms

Akatu¹,

Khosravi²,

Mehedi³

et al. 2022

Preprint

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The dynamics of suspended sediment involves inherent non-linearity and complexity as a result of the presence of both spatial variability of the basin characteristics and temporal climatic patterns. As a result of this complexity, the conventional sediment rating curve (SRC) and other empirical methods produce inaccurate predictions. Deep neural networks (DNNs) have emerged as one of the advanced modeling techniques capable of addressing inherent non-linearity in hydrological processes over the last few decades. DNN algorithms are used to perform predictive analysis and investigate the interdependencies among the most pivotal water quantity and quality parameters i.e., discharge, suspended sediment concentration (SSC), and turbidity. In this study, the Long short-term memory (LSTM) algorithm of DNNs is used to model the discharge-suspended sediment relationship for the Stony Clove Creek. The simulations were run using primary data on discharge, SSC and turbidity. For the development of the DNN models and examining the effects of input vectors, combinations of different input vectors (namely discharge, and SSC) for the current and previous days are considered. Furthermore, a suitable modelling approach with an appropriate model input structure is suggested based on model performance indices for the training and testing phases. The performance of developed models is assessed using statistical indices such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Statistically, the performance of DNN-based models in simulating the daily SSC performed well with observed sediment concentration series data. The study demonstrates the suitability of the DNN approach for simulation and estimation of daily SSC, opening up new research avenues for applying hybrid soft computing models in hydrology.

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Discussion. Design Curves for Suspended Load Estimation.

Cited by 2 publications

References 1 publication

Deep Learning-Based Modeling of Daily Suspended Sediment Concentration and Discharge in Esopus Creek

Deep Learning-Based Modeling of Daily Suspended Sediment Concentration and Discharge in Esopus Creek

Demystifying the Relationship Between River Discharge and Suspended Sediment Using Exploratory Analysis and Deep Neural Network Algorithms

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