scholarly journals The potential of ensemble WT-EEMD-kernel extreme learning machine techniques for prediction suspended sediment concentration in successive points of river

2021 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Nasrin Aghajani ◽  
Roghayeh Ghasempour ◽  
Farhad Alizadeh
Keyword(s):  
Data Processing ◽  
Suspended Sediment ◽  
Extreme Learning Machine ◽  
Mississippi River ◽  
Suspended Sediment Concentration ◽  
Flow Characteristics ◽  
Sediment Concentration ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
State 1

Abstract Sediment transport is one of the most important issues in river engineering. In this study, the capability of the Kernel Extreme Learning Machine (KELM) approach for predicting the river daily Suspended Sediment Concentration (SSC) and Discharge (SSD) was assessed. Three successive hydrometric stations of Mississippi river were considered and based on the sediment and flow characteristics during the period of 2005–2008. Several models were developed and tested for SSC and SSD modeling. For improving the applied model efficiency, two post-processing techniques, namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD), were used. Also, two states of modeling based on stations own data (state 1) and previous stations data (state 2) were considered. The single and integrated KELM models results comparison indicated that the integrated WT and EEMD-KELM models resulted in more accurate outcomes. Results showed that data processing with WT was more effective than EEMD in increasing the models efficiency. Data processing enhanced the models capability by up to 15%. The results showed that the state 1 modeling led to better results, however, using the integrated KELM approaches the previous stations data could be applied successfully for SSC and SSD modeling when the stations own data were not available. HIGHLIGHT The suspended sediment concentration (SSC) and suspended sediment discharge (SSD) were predicted via artificial intelligence approach in successive hydrometric stations. The data pre-processing impacts on models efficiency improving was assessed. The sensitivity analysis showed the most effective subseries obtained from pre-processing models.

scholarly journals Suspended sediment load prediction in consecutive stations of river based on ensemble pre-post-processing kernel based approaches

2021 ◽  
Author(s):  
Roghayeh Ghasempour ◽  
Kiyoumars Roushangar ◽  
Parveen Sihag
Keyword(s):  
Suspended Sediment ◽  
Extreme Learning Machine ◽  
Mississippi River ◽  
Flow Characteristics ◽  
Gaussian Process Regression ◽  
Accurate Estimation ◽  
Post Processing ◽  
Load Prediction ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Abstract Sediment transportation and accurate estimation of its rate is a significant issue for the river engineers and researchers. In this study, the capability of kernel based approaches including Kernel Extreme Learning Machine (KELM) and Gaussian Process Regression (GPR) was assessed for predicting the river daily Suspended Sediment Discharge (SSD). For this aim, Mississippi river with three consecutive hydrometric stations was selected as the case study. Based on the sediment and flow characteristics during the period of 2005–2008 several models were developed and tested under two scenarios (i.e. modeling based on each station's own data or previous stations' data). Two post-processing techniques namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD) were used for enhancing the SSD modeling capability. Also, data post-proceeding was done using Simple Linear Averaging (SLAM) and Nonlinear Kernel Extreme Learning Machine Ensemble (NKELME) methods. Obtained results indicated that the integrated models resulted in more accurate outcomes. Data processing enhanced the models capability up to 35%. It was found that the SSD modeling based on station's own data led to better results; however, using the integrated approaches the previous stations data could be applied successfully for the SSD modeling when station's own data were not available.

scholarly journals Suspended Sediment Concentration Estimation from Landsat Imagery along the Lower Missouri and Middle Mississippi Rivers Using an Extreme Learning Machine

2018 ◽  
Vol 10 (10) ◽  
pp. 1503 ◽  
Author(s):  
Kyle Peterson ◽  
Vasit Sagan ◽  
Paheding Sidike ◽  
Amanda Cox ◽  
Megan Martinez
Keyword(s):  
Neural Network ◽  
Suspended Sediment ◽  
Extreme Learning Machine ◽  
Suspended Sediment Concentration ◽  
Landsat Imagery ◽  
Sediment Concentration ◽  
Landsat 8 ◽  
Fluvial Systems ◽  
Spatial Coverage ◽  
Learning Machine

Monitoring and quantifying suspended sediment concentration (SSC) along major fluvial systems such as the Missouri and Mississippi Rivers provide crucial information for biological processes, hydraulic infrastructure, and navigation. Traditional monitoring based on in situ measurements lack the spatial coverage necessary for detailed analysis. This study developed a method for quantifying SSC based on Landsat imagery and corresponding SSC data obtained from United States Geological Survey monitoring stations from 1982 to present. The presented methodology first uses feature fusion based on canonical correlation analysis to extract pertinent spectral information, and then trains a predictive reflectance–SSC model using a feed-forward neural network (FFNN), a cascade forward neural network (CFNN), and an extreme learning machine (ELM). The trained models are then used to predict SSC along the Missouri–Mississippi River system. Results demonstrated that the ELM-based technique generated R2 > 0.9 for Landsat 4–5, Landsat 7, and Landsat 8 sensors and accurately predicted both relatively high and low SSC displaying little to no overfitting. The ELM model was then applied to Landsat images producing quantitative SSC maps. This study demonstrates the benefit of ELM over traditional modeling methods for the prediction of SSC based on satellite data and its potential to improve sediment transport and monitoring along large fluvial systems.

scholarly journals COUPLED WAVE-BED DYNAMICS, ATCHAFALAYA SHELF, LOUISIANA

2012 ◽  
Vol 1 (33) ◽  
pp. 116
Author(s):  
Cihan Sahin ◽  
Ilgar Safak ◽  
Alexandru Sheremet
Keyword(s):  
Shear Stress ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Critical Shear Stress ◽  
Flow Characteristics ◽  
Sediment Concentration ◽  
Density Variation ◽  
Acoustic Backscatter ◽  
Concentration Profiles ◽  
Fluid Mud

Observations of waves, currents, suspended sediment concentration and acoustic backscatter are used to re-investigate the interaction between the combined wave-current flow and cohesive sediments on the muddy Atchafalaya inner shelf. Observations support the previously proposed bed reworking cycle by waves of mobilization and resuspension of bed sediment, erosion, deposition with fluid mud formation and consolidation. Suspended sediment concentration profiles are estimated based on the acoustic backscatter of a current profiler. A one-dimensional vertical bottom boundary model is used to reconstruct the vertical structure of the flow characteristics, and estimate parameters difficult to observe directly, such as bottom shear stress. Estimated bed position, concentration profiles and computed bottom stresses remarkably support the previous findings on the bottom stress-resuspension relation, critical shear stress for erosion and bed density variation throughout a storm.

scholarly journals The Simplified of Suspended Sediment Measurement Method in Natural River (Case study of Kuning River in Yogyakarta, Indonesia)

2019 ◽  
Vol 5 (3) ◽  
pp. 243
Author(s):  
Bambang Yulistiyanto ◽  
Bambang Kironoto ◽  
Bangun Giarto ◽  
Mariatul Kiptiah ◽  
Muhammad Lutfi Tantowi
Keyword(s):  
Suspended Sediment ◽  
Cross Sections ◽  
Suspended Sediment Concentration ◽  
Flow Characteristics ◽  
Preventive Measure ◽  
Suspended Sediments ◽  
Sediment Concentration ◽  
Middle Part ◽  
Sediment Discharge ◽  
Sediment Flow

The accumulation of suspended sediment reduces the capacity in the river and deteriorates the water quality. Kuning  River in Yogyakarta is one of the main rivers in Yogyakarta, Indonesia, which is currently facing the issue of suspended sediments. To reduce the effect of suspended sediment and determine a preventive measure, hence, it is necessary to study the characteristics of the suspended sediment flow. Therefore, this study aims to investigate the suspended sediment flow characteristics, i.e. the velocity, and the concentration profiles at specific points in the transverse direction of the channel as well as the correlation of the suspended sediment discharge. Thirty (30) profiles of velocity and suspended sediment concentration were measured at six different points along the Kuning River. Opcon probe was used to measure suspended sediment concentration, while the propeller current meter was used to measure mean point-velocity profiles. Results of this study show the suspended sediment discharge ratio, defined as  are higher in the middle part of the channel than the one near the edge of the channel. The position of z/B where the values of  1 occurs at z/B = 0,19 and z/B = 0,75, which depend on the irregularity of the channel cross-sections. For practical purposes, the depth-averaged velocity and suspended sediment concentration can be determined from 1, 2 and/or 3 points measurement at y = 0,2D, 0,4D and 0,8D.

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

Remaining Useful Life Prediction of Lithium-ion Batteries Using Multiple Kernel Extreme Learning Machine

2020 ◽  
Vol 13 ◽  
Author(s):  
Renxiong Liu
Keyword(s):  
Lithium Ion Batteries ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Prediction Methods ◽  
Multiple Kernel ◽  
Kernel Extreme Learning Machine ◽  
Useful Life ◽  
Learning Machine

Objective: Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL). Methods: In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is presented. The MKELM’s kernel keeps diversified by consisting multiple kernel functions including Gaussian kernel function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are optimized through differential evolution (DE) algorithm. Results : Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in terms of absolute error, relative accuracy and mean square error. Conclusion: The prediction results prove that the MKELM approach can accurately predict the battery RUL. Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction methods in terms of prediction accuracy.

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