Frequent and accurate estimation of suspended sediment concentration (SSC) in surface waters and hydraulic schemes is of prime importance for proper design, operation and management of many hydraulic ...projects. in the present study, a long short-term memory (LSTM) was considered for predicting daily suspended sediment concentration in a river. The LSTM extends recurrent neural network with memory cells, instead of recurrent units, to store and output information, easing the learning of temporal relationships on long time scales. To build the model, daily observed time series of river discharge (
Q
) and SSC in the Schuylkill River in the United States were used. The results of the proposed model were evaluated and compared with the feedforward neural network and the adaptive neuro fuzzy inference system models which were trained using three different learning algorithms and widely used in the literature for prediction of daily SSC. The comparison of prediction accuracy of the models demonstrated that the LSTM model could satisfactory predict SSC time series, and adequately estimate cumulative suspended sediment load (SSL).
Reservoir sedimentation is a critical issue worldwide, resulting in reduced storage volumes and, thus, reservoir efficiency. Moreover, sedimentation can also increase the flood risk at related ...facilities. In some cases, drawdown flushing of the reservoir is an appropriate management tool. However, there are various options as to how and when to perform such flushing, which should be optimized in order to maximize its efficiency and effectiveness. This paper proposes an innovative concept, based on an artificial neural network (ANN), to predict the volume of sediment flushed from the reservoir given distinct input parameters. The results obtained from a real-world study area indicate that there is a close correlation between the inputs—including peak discharge and duration of flushing—and the output (i.e., the volume of sediment). The developed ANN can readily be applied at the real-world study site, as a decision-support system for hydropower operators.
The modeling and prediction of suspended sediment in a river are key elements in global water recourses and environment policy and management. In the present study, an Adaptive Neuro-Fuzzy Inference ...System model trained with the Levenberg-Marquardt learning algorithm is considered for time series modeling of suspended sediment concentration in a river. The model is trained and validated using daily river discharge and suspended sediment concentration data from the Schuylkill River in the United States. The results of the proposed method are evaluated and compared with similar networks trained with the common Hybrid and Back-Propagation algorithms, which are widely used in the literature for prediction of suspended sediment concentration. Obtained results demonstrate that models trained with the Hybrid and Levenberg-Marquardt algorithms are comparable in terms of prediction accuracy. However, the networks trained with the Levenberg-Marquardt algorithm perform better than those trained with the Hybrid approach.
Fine sediment infiltration and subsequent clogging in a gravel bed affect several fluvial, ecological, and biological processes, resulting in the degradation of the river ecosystem. Despite many ...experimental and a few numerical studies, the process is yet to be entirely understood. We employed a pure Lagrangian framework, called the Discrete Element Method (DEM), to numerically investigate the infiltration process. Special attention is given to tackling the issue of non-spherical and irregular particle shapes and particle size distributions (PSDs) in numerical simulations. Due to computational limitations, these aspects were either not considered or simplified in previous numerical studies. We implicitly included non-spherical and irregular shape effects through rolling resistance models, which do not cause excessive computational overhead. Our study shows that rolling resistance models greatly influence packing and fine sediment infiltration. However, they may also lead to unphysical particle behavior; thus, they should be carefully used in numerical simulations. Oversimplified PSDs do not mirror natural systems, and full PSDs pose computational challenges. Sufficient grain classes are needed to mimic the non-homogeneity and poly-dispersity found in natural fluvial sediments. The infiltrating characteristics of sand concerning PSD and shape effects are linked to size ratio D15,Gravel/D85,Sand, assuring physical and realistic modeling of the infiltration process.
CFD-DEM modelling of particle-laden turbulent flow is challenging in terms of the required and obtained CFD resolution, heavy DEM computations, and the limitations of the method. Here, we assess the ...efficiency of a particle-tracking solver in OpenFOAM with RANS-DEM and LES-DEM approaches under the unresolved CFD-DEM framework. Furthermore, we investigate aspects of the unresolved CFD-DEM method with regard to the coupling regime, particle boundary condition and turbulence modelling. Applying one-way and two-way coupling to our RANS-DEM simulations demonstrates that it is sufficient to include one-way coupling when the particle concentration is small (O ~ 10−5). Moreover, our study suggests an approach to estimate the particle boundary condition for cases when data is unavailable. In contrast to what has been previously reported for the adopted case, our RANS-DEM results demonstrate that simple dispersion models considerably underpredict particle dispersion and previously observed reasonable particle dispersion were due to an error in the numerical setup rather than the used dispersion model claiming to include turbulence effects on particle trajectories. LES-DEM may restrict extreme mesh refinement, and, under such scenarios, dynamic LES turbulence models seem to overcome the poor performance of static LES turbulence models. Sub-grade scale effects cannot be neglected when using coarse mesh resolution in LES-DEM and must be recovered with efficient modelling approaches to predict accurate particle dispersion.
In gravel-bed rivers, monitoring porosity is vital for fluvial geomorphology assessment as well as in river ecosystem management. Conventional porosity prediction methods are restricting in terms of ...the number of considered factors and are also time-consuming. We present a framework, the combination of the Discrete Element Method (DEM) and Artificial Neural Network (ANN), to study the relationship between porosity and the grain size distribution. DEM was applied to simulate the 3D structure of the packing gravel-bed and fine sediment infiltration processes under various forces. The results of the DEM simulations were verified with the experimental data of porosity and fine sediment distribution. Further, an algorithm was developed for calculating high-resolution results of porosity and grain size distribution in vertical and horizontal directions from the DEM results, which were applied to develop a Feed Forward Neural Network (FNN) to predict bed porosity based on grain size distribution. The reliable results of DEM simulation and FNN prediction confirm that our framework is successful in predicting porosity change of gravel-bed.
Understanding the alterations of gravel bed structures, sediment transport, and the effects on aquatic habitat play an essential role in eco-hydraulic and sediment transport management. In recent ...years, the evaluation of changes of void in bed materials has attracted more concern. However, analyzing the morphological changes and grain size distribution that are associated with the porosity variations in gravel-bed rivers are still challenging. This study develops a new model using a multi-layer’s concept to simulate morphological changes and grain size distribution, taking into account the porosity variabilities in a gravel-bed river based on the mass conservation for each size fraction and the exchange of fine sediments between the surface and subsurface layers. The Discrete Element Method (DEM) is applied to model infiltration processes and to confirm the effects of the relative size of fine sediment to gravel on the infiltration depth. Further, the exchange rate and the bed porosity are estimated while using empirical formulae. The new model was tested on three straight channels. Analyzing the calculated results and comparing with the observed data show that the new model can successfully simulate sediment transport, grain sorting processes, and bed change in gravel-bed rivers.
The worldwide storage volume of reservoirs is estimated to decrease by 0.5–1% per year due to sedimentation, which is higher than the gain in volume by newly built dams. For water supply or flood ...protection, the preservation of the storage volume is crucial. Operators and authorities, therefore, need sediment management concepts to ensure that the storage volume is sufficient. In this study, we developed a sediment-flushing concept using 2D numerical modelling for a run-of-river hydropower plant located in the Saalach River in southeastern Germany. The calibrated bed elevation was used as the initial bed for a number of simulations with different discharge regimes under varying operational schemes. By comparing the simulated results, we propose an appropriate flushing scheme in terms of intensity and duration to obtain a balance of sediment regime in the river. Furthermore, we demonstrate that such an optimised sediment management can generate synergies for improving measures of flood protection.
Large amounts of fine sediment infiltration into void spaces of coarse bed material have the ability to alter the morphodynamics of rivers and their aquatic ecosystems. Modelling the mechanisms of ...fine sediment infiltration in gravel-bed is therefore of high significance. We proposed a framework for calculating the sediment exchange in two layers. On the basis of the conventional approaches, we derived a two-layer fine sediment sorting, which considers the transportation of fine sediment in the form of infiltration into the void spaces of the gravel-bed. The relationship between the fine sediment exchange and the affected factors was obtained by using the discrete element method (DEM) in combination with feedforward neural networks (FNN). The DEM model was validated and applied for gravel-bed flumes with different sizes of fine sediments. Further, we developed algorithms for extracting information in terms of gravel-bed packing, grain size distribution, and porosity variation. On the basis of the DEM results with this extracted information, we developed an FNN model for fine sediment sorting. Analyzing the calculated results and comparing them with the available measurements showed that our framework can successfully simulate the exchange of fine sediment in gravel-bed rivers.
Salinity intrusion in the Vietnamese Mekong Delta (VMD) has been exacerbated significantly in recent years by the changing upstream inflows, sea level rise resulting from climate change, and ...socioeconomic development activities. Despite significant damage to agricultural production and freshwater supplies, quantitative assessments of future flows and salinization remain limited due to lack of observation data and modelling tools to represent a highly complex hydraulic network. In this study, we combine 1D-MIKE 11 and 2D-MIKE 21 hydrodynamic models to simulate future flows, water level and salinity intrusion in the Hau River—one main river branch in the Mekong Delta. Future hydrological changes are simulated under multiple scenarios of upstream inflow changes, climate change and sea level rise for the 2036–2065 period. We first use the 1D-MIKE 11 to simulate the flow regime throughout the whole VMD using upstream discharges, outlet water levels and rainfall data as boundary conditions. Output from this step is then used to force the 2D-MIKE 21 model to estimate flow velocity, water level and salinity concentration in the Hau River, focusing on the salinization-prone section between Can Tho, Dinh An, and Tran De estuaries. Simulation results show that salinization will increase substantially, characterized by (1) higher salinity intrusion length under spring tide from 6.78% to 7.97%, and 8.62% to 10.89% under neap tide; and (2) progression of the salinity isohalines towards the upper Mekong Delta, from 3.29 km to 3.92 km for 1 practical salinity unit (PSU) under spring tide, and 4.36 km to 4.65 km for 1 PSU concentration under neap tide. Additionally, we found that salinity intrusion will make it more difficult to re-establish the freshwater condition in the estuary in the future. In particular, the flushing time required to replace saltwater with freshwater at the estuaries tends to increase to between 7.27 h for maximum discharge of 4500 m3/s and 58.95 h for discharge of 400 m3/s under the most extreme scenario. Increasing salinization along the Hau River will have important consequences for crop production, freshwater supplies and freshwater ecosystems, therefore requiring timely adaptation responses.
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