Version 1
: Received: 16 May 2023 / Approved: 17 May 2023 / Online: 17 May 2023 (02:20:22 CEST)
How to cite:
Khosravi, M.; Ghoochani, S.; Nazemi, N. Deep Learning-Based Modeling of Daily Suspended Sediment Concentration and Discharge in Esopus Creek. Preprints2023, 2023051167. https://doi.org/10.20944/preprints202305.1167.v1
Khosravi, M.; Ghoochani, S.; Nazemi, N. Deep Learning-Based Modeling of Daily Suspended Sediment Concentration and Discharge in Esopus Creek. Preprints 2023, 2023051167. https://doi.org/10.20944/preprints202305.1167.v1
Khosravi, M.; Ghoochani, S.; Nazemi, N. Deep Learning-Based Modeling of Daily Suspended Sediment Concentration and Discharge in Esopus Creek. Preprints2023, 2023051167. https://doi.org/10.20944/preprints202305.1167.v1
APA Style
Khosravi, M., Ghoochani, S., & Nazemi, N. (2023). Deep Learning-Based Modeling of Daily Suspended Sediment Concentration and Discharge in Esopus Creek. Preprints. https://doi.org/10.20944/preprints202305.1167.v1
Chicago/Turabian Style
Khosravi, M., Shima Ghoochani and Neda Nazemi. 2023 "Deep Learning-Based Modeling of Daily Suspended Sediment Concentration and Discharge in Esopus Creek" Preprints. https://doi.org/10.20944/preprints202305.1167.v1
Abstract
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.
Keywords
Water quality; suspended sediment; discharge; LSTM; deep neural network
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
