Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Intelligent Approaches in Predicting Hydrodynamics and Sediment Transport
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Intelligent Approaches in Predicting Hydrodynamics and Sediment Transport

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 15632

Special Issue Editor

Dr. Kelin Hu

E-Mail Website
Guest Editor
Department of River-Coastal Science and Engineering, Tulane University, 6823 St. Charles Ave., New Orleans, LA 70118, USA
Interests: modeling of storm surge, hurricane waves, and sediment transport and morphological developments in coastal and estuarine areas
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The objective of this Special Issue is to introduce intelligent and innovative approaches in predicting hydrodynamics and sediment transport in riverine, coastal, and estuarine areas. These approaches may cover, but are not limited to, aspects of data collection, model development/improvement, process parametrization, numerical experiment design, and results analysis. We encourage studies of stage/water level, streamflow/current, storm surge, waves, salinity, temperature, sediment transport, short- and long-term morphological changes in rivers, lakes, bays, deltas, and coasts. We are also interested in the impacts of extreme events (e.g., flood, cold fronts, and tropical storm), land subsidence, sea level rise and human activities (e.g., deep waterway projects, sediment diversion projects, navigation channel dredging, and hydraulic structures). The applications of new techniques/methodologies in modeling, such as data assimilation, remote sensing, neutral network, machine learning, and high-performance computing, are especially welcome.   

Prof. Dr. Kelin Hu
Guest Editor

Keywords

  • modeling
  • hydrodynamics
  • sediment transport
  • morphological change
  • estuaries and coasts
  • storm surge
  • human impacts

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Related Special Issue

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

13 pages, 8822 KiB  
Article
The Impact of Levee Openings on Storm Surge: A Numerical Analysis in Coastal Louisiana
by Kelin Hu, Ehab Meselhe, Rachel Rhode, Natalie Snider and Alisha Renfro
Appl. Sci. 2022, 12(21), 10884; https://doi.org/10.3390/app122110884 - 27 Oct 2022
Cited by 4 | Viewed by 1311
Abstract
The existence of the Mississippi River (MR) and Tributaries’ levees in coastal Louisiana could block storm surge and cause surge setup in adjacent basins. In order to reduce storm surge amplification caused by these barriers, one possible solution is to build “floodways” through [...] Read more.
The existence of the Mississippi River (MR) and Tributaries’ levees in coastal Louisiana could block storm surge and cause surge setup in adjacent basins. In order to reduce storm surge amplification caused by these barriers, one possible solution is to build “floodways” through the mainstem MR levees to allow surge during tropical events to cross. The primary purpose of this study is to examine if these floodways/openings can help reduce storm surge in adjacent basins. Using Hurricane Isaac (2012) as an example, a pre-validated Delft3D-based hydrodynamic model was applied to study the effect of levee openings on storm surge. Model results and flux analysis show that these levee openings were not effective in reducing storm surge in Barataria Basin and Breton Sound due to the complex interaction between the cross flow from the surge and the MR flow. During Isaac, the MR water could be diverted to Barataria and/or Breton, which resulted in an increase in storm surge, essentially defeating the primary objective of the levee openings. Overall, the impact of levee openings at the selected locations on storm surge reduction in adjacent basins of coastal Louisiana was minor and very limited. Full article
(This article belongs to the Special Issue Intelligent Approaches in Predicting Hydrodynamics and Sediment Transport)
►▼ Show Figures

Figure 1

17 pages, 4736 KiB  
Article
Prediction of Suspended Sediment Concentration Based on the Turbidity-Concentration Relationship Determined via Underwater Image Analysis
by Woochul Kang, Kyungsu Lee and Jongmin Kim
Appl. Sci. 2022, 12(12), 6125; https://doi.org/10.3390/app12126125 - 16 Jun 2022
Cited by 2 | Viewed by 2105
Abstract
Sediment measurement data are essential for sediment transport analysis and therefore highly important in overall river planning. Extant sediment measurement methods consume considerable manpower and time and are limited by factors including economic reasons and worker risks. This study primarily aimed to predict [...] Read more.
Sediment measurement data are essential for sediment transport analysis and therefore highly important in overall river planning. Extant sediment measurement methods consume considerable manpower and time and are limited by factors including economic reasons and worker risks. This study primarily aimed to predict the changes in SSC (Suspended Sediment Concentration) and turbidity by examining the change in color in underwater images. While maintaining a constant flow in a channel, the turbidity and concentration were measured under different SSC. Multiple regression models were developed using turbidity measurement results, and they exhibited high explanatory powers (adjusted R2 > 0.91). Furthermore, upon verification using the verification dataset of the experimental results, an excellent predictive power (RMSE ≈ 0.4 NTU) was demonstrated. The model with the highest predictive power, which was inclusive of red and green bands and showed no underlying multicollinearity was used to predict turbidity. Finally, the turbidity and suspended sediment concentration relationship determined from the experimental results was used to estimate the sediment concentration from the color changes in the underwater images. The concentrations that were predicted by the model showed satisfactory results, compared to the measurements (RMSE ≈ 21 ppm). This study indicated the feasibility of continuous SSC monitoring using underwater images as a new measurement method. Full article
(This article belongs to the Special Issue Intelligent Approaches in Predicting Hydrodynamics and Sediment Transport)
►▼ Show Figures

Figure 1

19 pages, 6682 KiB  
Article
Evaluation and Validation of Estimated Sediment Yield and Transport Model Developed with Model Tree Technique
by Woochul Kang, Kyungsu Lee and Eun-kyung Jang
Appl. Sci. 2022, 12(3), 1119; https://doi.org/10.3390/app12031119 - 21 Jan 2022
Cited by 8 | Viewed by 2014
Abstract
This study evaluated the applicability of existing sediment yield and transport estimation models developed using data mining classification and prediction techniques and validated them. Field surveys were conducted by using an acoustic Doppler current profiler and laser in situ scattering and transmission at [...] Read more.
This study evaluated the applicability of existing sediment yield and transport estimation models developed using data mining classification and prediction techniques and validated them. Field surveys were conducted by using an acoustic Doppler current profiler and laser in situ scattering and transmission at measuring points in the main stream of the Nakdong River located where the tributaries of the Geumho, Hwang, and Nam Rivers join. Surveys yielded estimations of water velocity, discharge, and suspended sediment concentrations were measured. In contrast with models based on the general watershed characteristics factors, some models based on hydraulic explanatory flow variables demonstrated an excellent predictability. This is because the selected submodels for validation, which provided excellent prediction results, were based on a large number of calibration data. It indicates that a sufficient number of reliable data is required in developing a sediment yield estimation model using data mining. For practical applications of data mining to extant sediment yield estimation models, comprehensive considerations are required, including the purpose and background of model development, and data range. Furthermore, the existing models should be periodically updated with the consideration of temporal and spatial lumping problems. Full article
(This article belongs to the Special Issue Intelligent Approaches in Predicting Hydrodynamics and Sediment Transport)
►▼ Show Figures

Figure 1

11 pages, 30415 KiB  
Article
Prediction of River Sediment Transport Based on Wavelet Transform and Neural Network Model
by Zongyu Li, Zhilin Sun, Jing Liu, Haiyang Dong, Wenhua Xiong, Lixia Sun and Hanyu Zhou
Appl. Sci. 2022, 12(2), 647; https://doi.org/10.3390/app12020647 - 10 Jan 2022
Cited by 9 | Viewed by 2163
Abstract
The sedimentation problem is one of the critical issues affecting the long-term use of rivers, and the study of sediment variation in rivers is closely related to water resource, river ecosystem and estuarine delta siltation. Traditional research on sediment variation in rivers is [...] Read more.
The sedimentation problem is one of the critical issues affecting the long-term use of rivers, and the study of sediment variation in rivers is closely related to water resource, river ecosystem and estuarine delta siltation. Traditional research on sediment variation in rivers is mostly based on field measurements and experimental simulations, which requires a large amount of human and material resources, many influencing factors and other restrictions. With the development of computer technology, intelligent approaches have been applied to hydrological models to establish small information in river areas. In this paper, considering the influence of multiple factors on sediment transport, the validity of predicting sediment transport combined with wavelet transforms and neural network was analyzed. The rainfall and runoff cycles are extracted and decomposed into time series sub-signals by wavelet transforms; then, the data post-processing is used as the neural network training set to predict the sediment model. The results show that wavelet coupled neural network model effectively improves the accuracy of the predicted sediment model, which can provide a reference basis for river sediment prediction. Full article
(This article belongs to the Special Issue Intelligent Approaches in Predicting Hydrodynamics and Sediment Transport)
►▼ Show Figures

Figure 1

27 pages, 6162 KiB  
Article
Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
by Muhammad Adnan Khan, Jürgen Stamm and Sajjad Haider
Appl. Sci. 2021, 11(18), 8290; https://doi.org/10.3390/app11188290 - 7 Sep 2021
Cited by 9 | Viewed by 2027
Abstract
A key goal of sediment management is the quantification of suspended sediment load (SSL) in rivers. This research focused on a comparison of different means of suspended sediment estimation in rivers. This includes sediment rating curves (SRC) and soft computing techniques, i.e., local [...] Read more.
A key goal of sediment management is the quantification of suspended sediment load (SSL) in rivers. This research focused on a comparison of different means of suspended sediment estimation in rivers. This includes sediment rating curves (SRC) and soft computing techniques, i.e., local linear regression (LLR), artificial neural networks (ANN) and the wavelet-cum-ANN (WANN) method. Then, different techniques were applied to predict daily SSL at the Pirna and Magdeburg Stations of the Elbe River in Germany. By comparing the results of all the best models, it can be concluded that the soft computing techniques (LLR, ANN and WANN) better predicted the SSL than the SRC method. This is due to the fact that the former employed non-linear techniques for the data series reconstruction. The WANN models were the overall best performer. The WANN models in the testing phase showed a mean R2 of 0.92 and a PBIAS of −0.59%. Additionally, they were able to capture the suspended sediment peaks with greater accuracy. They were more successful as they captured the dynamic features of the non-linear and time-variant suspended sediment load, while other methods used simple raw data. Thus, WANN models could be an efficient technique to simulate the SSL time series because they extract key features embedded in the SSL signal. Full article
(This article belongs to the Special Issue Intelligent Approaches in Predicting Hydrodynamics and Sediment Transport)
►▼ Show Figures

Figure 1

17 pages, 3618 KiB  
Article
The Visual Measurement of Velocity Profile Distribution in Silt Carrying Flow by Using Ultrasound PIV and Iterative Multi-Grid Deformation Technique
by Xianjian Zou, Wenbin Hu, Huan Song and Bingrui Chen
Appl. Sci. 2021, 11(15), 6952; https://doi.org/10.3390/app11156952 - 28 Jul 2021
Cited by 5 | Viewed by 1919
Abstract
Flow velocity in silt carrying flow is one key parameter to many river engineering problems. A visual measurement technique of velocity profile distribution in silt carrying flow is provided using a portable ultrasound imaging system and an improved iterative multi-grid deformation algorithm. A [...] Read more.
Flow velocity in silt carrying flow is one key parameter to many river engineering problems. A visual measurement technique of velocity profile distribution in silt carrying flow is provided using a portable ultrasound imaging system and an improved iterative multi-grid deformation algorithm. A convex array probe in the system is used to obtain a series of ultrasonic images at different times. Window offset and an iterative computing scheme for reducing interrogation window size in the algorithm improve the accuracy and efficiency of flow velocity measurement in regions with velocity gradients. Results show that the measured profile velocities can be more acceptable after being compared with time-averaged stream-wise velocities of profiles at ten positions in the same silt carrying flow and subsequently verified by comparing the point-by-point standard value. The measured velocity is more in agreement with the theoretical value, with the minimum root mean square error in the ultrasound beam sweep effect calculated by using optimal interrogation size parameters. The system is a feasible alternative to the single-point measurement technique in silt carrying flow. The iterative multi-grid deformation algorithm can analyze velocity profile distribution with gradients simultaneously, which can help the real-time measurement of multiple spatial velocity distribution and turbulence. Full article
(This article belongs to the Special Issue Intelligent Approaches in Predicting Hydrodynamics and Sediment Transport)
►▼ Show Figures

Graphical abstract

Review

Jump to: Research

20 pages, 2001 KiB  
Review
Expanding the Sediment Transport Tracking Possibilities in a River Basin through the Development of a Digital Platform—DNS/SWAT
by Paweł Wilk
Appl. Sci. 2022, 12(8), 3848; https://doi.org/10.3390/app12083848 - 11 Apr 2022
Cited by 5 | Viewed by 2637
Abstract
Simulation of stochastic and variable sediment transport processes within models still poses a big challenge, especially in mountainous areas. Since sediment transport, including erosion and deposition, remains an unceasing problem in many areas, sediment modeling is perceived as a possible solution. This article [...] Read more.
Simulation of stochastic and variable sediment transport processes within models still poses a big challenge, especially in mountainous areas. Since sediment transport, including erosion and deposition, remains an unceasing problem in many areas, sediment modeling is perceived as a possible solution. This article combines a review of the selected sediment models with a presentation of the effects of several years of research using the DNS digital platform in the Western Carpathians. The review focuses on the main advantages and gaps in selected modeling tools with particular emphasis on one of the most popular: SWAT. The description of the digital platform—DNS is an example of how to answer these gaps by combining subsequent models, methods, and databases using their best features. To accentuate the benefits of such an approach, the effects of combining subsequent models (AdH/PTM) and methods (fingerprinting) on a common digital DNS space are presented, on the example of the Raba River (basin). In this way, both unique possibilities of estimating the amount of contamination carried with sediment particles and their sources, as well as sequencing of sedimentation in the reservoir, taking into account its subsequent zones, were obtained. Full article
(This article belongs to the Special Issue Intelligent Approaches in Predicting Hydrodynamics and Sediment Transport)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop