Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Improving Flow Discharge-Suspended Sediment Relations: Intelligent Algorithms versus Data Separation
Information on the transport of fluvial suspended sediment loads (SSL) is crucial due to its effects on water quality, pollutant transport and transformation, dam operations, and reservoir capacity. As such, adopting a reliable method to accurately estimate SSL is a key topic for watershed managers, hydrologists, river engineers, and hydraulic engineers. One of the most common methods for estimating SSL or suspended sediment concentrations (SSC) is sediment rating curve (SRC), which has several weaknesses. Here, we optimize the SRC equation using two main approaches. Firstly, three well recognized metaheuristic algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competitive algorithm (ICA)) were used together with two classical approaches (food and agriculture organization (FAO) and non-parametric smearing estimator (CF2)) to optimize the coefficients of the SRC regression model. The second approach uses separation of data based on season and flow discharge (Qw) characteristics. A support vector regression (SVR) model using only Qw as an input was employed for SSC estimation and the results were compared with the SRC and its optimized versions. Metaheuristic algorithms improved the performance of the SRC model and the PSO model outperformed the other algorithms. These results also indicate that the model performance was directly related to the temporal separation of data. Based on these findings, if data are more homogenous and related to the limited climatic conditions used in the estimation of SSC, the estimations are improved. Moreover, it was observed that optimizing SRC through metaheuristic models was much more effective than separating data in the SCR model. The results also indicated that with the same input data, SVR was superior to the SRC model and its optimized version.
Improving Flow Discharge-Suspended Sediment Relations: Intelligent Algorithms versus Data Separation
Information on the transport of fluvial suspended sediment loads (SSL) is crucial due to its effects on water quality, pollutant transport and transformation, dam operations, and reservoir capacity. As such, adopting a reliable method to accurately estimate SSL is a key topic for watershed managers, hydrologists, river engineers, and hydraulic engineers. One of the most common methods for estimating SSL or suspended sediment concentrations (SSC) is sediment rating curve (SRC), which has several weaknesses. Here, we optimize the SRC equation using two main approaches. Firstly, three well recognized metaheuristic algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competitive algorithm (ICA)) were used together with two classical approaches (food and agriculture organization (FAO) and non-parametric smearing estimator (CF2)) to optimize the coefficients of the SRC regression model. The second approach uses separation of data based on season and flow discharge (Qw) characteristics. A support vector regression (SVR) model using only Qw as an input was employed for SSC estimation and the results were compared with the SRC and its optimized versions. Metaheuristic algorithms improved the performance of the SRC model and the PSO model outperformed the other algorithms. These results also indicate that the model performance was directly related to the temporal separation of data. Based on these findings, if data are more homogenous and related to the limited climatic conditions used in the estimation of SSC, the estimations are improved. Moreover, it was observed that optimizing SRC through metaheuristic models was much more effective than separating data in the SCR model. The results also indicated that with the same input data, SVR was superior to the SRC model and its optimized version.
Improving Flow Discharge-Suspended Sediment Relations: Intelligent Algorithms versus Data Separation
Haniyeh Asadi (Autor:in) / Mohammad T. Dastorani (Autor:in) / Roy C. Sidle (Autor:in) / Kaka Shahedi (Autor:in)
2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
Open-channel flow with suspended sediment particles
| British Library Conference Proceedings | 1995
Assessment of suspended sediment discharge in the Purus River basin, Brazil
| Online Contents | 2016
Assessment of suspended sediment discharge in the Purus River basin, Brazil
| Taylor & Francis Verlag | 2016
Suspended sediment prediction using two different feed-forward back-propagation algorithms
| Online Contents | 2007