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 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.

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.

scholarly journals A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2599
Author(s):  
Zhenbao Li ◽  
Wanlu Jiang ◽  
Sheng Zhang ◽  
Yu Sun ◽  
Shuqing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Recognition Accuracy ◽  
Ensemble Empirical Mode Decomposition ◽  
Hydraulic Pump ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

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