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13 pages, 8822 KiB

Open AccessArticle

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 3 | Viewed by 1080

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)

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17 pages, 4736 KiB

Open AccessArticle

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 1741

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)

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19 pages, 6682 KiB

Open AccessArticle

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 7 | Viewed by 1714

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 develo** 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 lum** problems. Full article

(This article belongs to the Special Issue Intelligent Approaches in Predicting Hydrodynamics and Sediment Transport)

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11 pages, 30415 KiB

Open AccessArticle

Prediction of River Sediment Transport Based on Wavelet Transform and Neural Network Model

by Zongyu Li, Zhilin Sun, **g Liu, Haiyang Dong, Wenhua **ong, 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 1879

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)

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27 pages, 6162 KiB

Open AccessArticle

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 1784

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)

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17 pages, 3618 KiB

Open AccessFeature PaperArticle

The Visual Measurement of Velocity Profile Distribution in Silt Carrying Flow by Using Ultrasound PIV and Iterative Multi-Grid Deformation Technique

by **anjian 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 1649

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)

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Review

Jump to: Research

20 pages, 2001 KiB

Open AccessReview

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 2303

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)

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