Version 1
: Received: 21 November 2022 / Approved: 23 November 2022 / Online: 23 November 2022 (07:28:27 CET)
Version 2
: Received: 26 November 2022 / Approved: 28 November 2022 / Online: 28 November 2022 (13:43:22 CET)
Version 3
: Received: 14 December 2022 / Approved: 16 December 2022 / Online: 16 December 2022 (08:08:08 CET)
How to cite:
Akatu, W.; Khosravi, M.; Mehedi, M.A.A.; Mantey, J.; Tohidi, H.; Shabanian, H. Demystifying the Relationship Between River Discharge and Suspended Sediment Using Exploratory Analysis and Deep Neural Network Algorithms. Preprints2022, 2022110437. https://doi.org/10.20944/preprints202211.0437.v2
Akatu, W.; Khosravi, M.; Mehedi, M.A.A.; Mantey, J.; Tohidi, H.; Shabanian, H. Demystifying the Relationship Between River Discharge and Suspended Sediment Using Exploratory Analysis and Deep Neural Network Algorithms. Preprints 2022, 2022110437. https://doi.org/10.20944/preprints202211.0437.v2
Akatu, W.; Khosravi, M.; Mehedi, M.A.A.; Mantey, J.; Tohidi, H.; Shabanian, H. Demystifying the Relationship Between River Discharge and Suspended Sediment Using Exploratory Analysis and Deep Neural Network Algorithms. Preprints2022, 2022110437. https://doi.org/10.20944/preprints202211.0437.v2
APA Style
Akatu, W., Khosravi, M., Mehedi, M.A.A., Mantey, J., Tohidi, H., & Shabanian, H. (2022). Demystifying the Relationship Between River Discharge and Suspended Sediment Using Exploratory Analysis and Deep Neural Network Algorithms. Preprints. https://doi.org/10.20944/preprints202211.0437.v2
Chicago/Turabian Style
Akatu, W., Hamed Tohidi and Hanieh Shabanian. 2022 "Demystifying the Relationship Between River Discharge and Suspended Sediment Using Exploratory Analysis and Deep Neural Network Algorithms" Preprints. https://doi.org/10.20944/preprints202211.0437.v2
Abstract
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.
Keywords
deep neural network; long short-term memory; suspended sediment; discharge
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.
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Commenter: Marzieh Khosravi
Commenter's Conflict of Interests: Author