scholarly journals Evaluation of the impact of channel geometry and rough elements arrangement in hydraulic jump energy dissipation via SVM

2018 ◽  
Vol 21 (1) ◽  
pp. 92-103 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Roghayeh Ghasempour
Keyword(s):  
Energy Dissipation ◽  
Dimensional Analysis ◽  
Hydraulic Jump ◽  
Relative Energy ◽  
Superior Performance ◽  
Support Vector ◽  
Data Series ◽  
Rough Boundary ◽  
Channel Geometry ◽  
The Impact

Abstract Rough bed channels are one of the appurtenances used to dissipate the extra energy of the flow through hydraulic jump. The aim of this paper is to assess the effects of channel geometry and rough boundary conditions (i.e., rectangular, trapezoidal, and expanding channels with different rough elements) in predicting the hydraulic jump energy dissipation using support vector machine (SVM) as a meta-model approach. Using different experimental data series, different models were developed with and without considering dimensional analysis. The results approved capability of the SVM model in predicting the relative energy dissipation. It was found that the developed models for expanding channel with central sill performed more successfully and, for this case, superior performance was obtained for the model with parameters Fr1 and h1/B. Considering the rectangular and trapezoidal channels, the model with parameters Fr1, (h2−h1)/h1, W/Z led to better predictions. It was observed that between two types of strip and staggered rough elements, strip type led to more accurate results. The obtained results showed that the developed models for the case of simulation based on dimensional analysis yielded better predictions. The sensitivity analysis results showed that Froude number had the most significant impact on the modeling.

scholarly journals Predicting the relative energy dissipation of hydraulic jump in rough and smooth bed compound channels using SVM

2018 ◽  
Vol 19 (4) ◽  
pp. 1110-1119
Author(s):  
Seyed Mahdi Saghebian
Keyword(s):  
Energy Dissipation ◽  
Hydraulic Jump ◽  
Dominant Role ◽  
Rectangular Channel ◽  
Relative Energy ◽  
Support Vector ◽  
Channel Models ◽  
Rough Bed ◽  
Different Shapes ◽  
Rough Beds

Abstract Channels with different shapes and bed conditions are used as useful appurtenances to dissipate the extra energy of a hydraulic jump. Accurate prediction of hydraulic jump energy dissipation is important in design of hydraulic structures. In the current study, hydraulic jump energy dissipation was assessed in channels with different shapes and bed conditions (i.e. smooth and rough beds) using the support vector machine (SVM) as an intelligence approach. Five series of experimental datasets were applied to develop the models. The results showed that the SVM model is successful in estimating the relative energy dissipation. For the smooth bed, it was observed that the sloping channel models with steps performed more successfully than rectangular and trapezoidal channels and the step height is an effective variable in the estimation process. For the rough bed, the trapezoidal channel models were more accurate than the rectangular channel. It was found that rough element geometry is effective in estimation of the energy dissipation. The result showed that the models of rough channels led to better predictions. The sensitivity analysis results revealed that Froude number had the more dominant role in the modeling. Comparison among SVM and two other intelligence approaches showed that SVM is more successful in the prediction process.

scholarly journals Estimation of hydraulic jump characteristics of channels with sudden diverging side walls via SVM

2017 ◽  
Vol 76 (7) ◽  
pp. 1614-1628 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Reyhaneh Valizadeh ◽  
Roghayeh Ghasempour
Keyword(s):  
Froude Number ◽  
Hydraulic Jump ◽  
Relative Energy ◽  
Expansion Ratio ◽  
Superior Performance ◽  
Support Vector ◽  
Great Performance ◽  
Stilling Basins ◽  
Side Walls ◽  
Sequent Depth

Sudden diverging channels are one of the energy dissipaters which can dissipate most of the kinetic energy of the flow through a hydraulic jump. An accurate prediction of hydraulic jump characteristics is an important step in designing hydraulic structures. This paper focuses on the capability of the support vector machine (SVM) as a meta-model approach for predicting hydraulic jump characteristics in different sudden diverging stilling basins (i.e. basins with and without appurtenances). In this regard, different models were developed and tested using 1,018 experimental data. The obtained results proved the capability of the SVM technique in predicting hydraulic jump characteristics and it was found that the developed models for a channel with a central block performed more successfully than models for channels without appurtenances or with a negative step. The superior performance for the length of hydraulic jump was obtained for the model with parameters F1 (Froude number) and (h2—h1)/h1 (h1 and h2 are sequent depth of upstream and downstream respectively). Concerning the relative energy dissipation and sequent depth ratio, the model with parameters F1 and h1/B (B is expansion ratio) led to the best results. According to the outcome of sensitivity analysis, Froude number had the most significant effect on the modeling. Also comparison between SVM and empirical equations indicated the great performance of the SVM.

scholarly journals Assessing the Capability of KELM Meta-Model Approach in Predicting the Energy Dissipation in Different Shapes Channels

Proceedings ◽  
2020 ◽  
Vol 63 (1) ◽  
pp. 45
Author(s):  
Seyed Mahdi Saghebian ◽  
Daniel Dragomir-Stanciu ◽  
Roghayeh Ghasempour
Keyword(s):  
Energy Dissipation ◽  
Hydraulic Jump ◽  
Rectangular Channel ◽  
Relative Energy ◽  
Data Series ◽  
Meta Model ◽  
Extra Energy ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Model Approach

For transition of a supercritical flow into a subcritical flow in an open channel, a hydraulic jump phenomenon is used. Different shaped channels are used as useful tools in the extra energy dissipation of the hydraulic jump. Accurate prediction of relative energy dissipation is important in designing hydraulic structures. The aim of this paper is to assess the capability of a Kernel extreme Learning Machine (KELM) meta-model approach in predicting the energy dissipation in different shaped channels (i.e., rectangular and trapezoidal channels). Different experimental data series were used to develop the models. The obtained results approved the capability of the KELM model in predicting the energy dissipation. Results showed that the rectangular channel led to better outcomes. Based on the results obtained for the rectangular and trapezoidal channels, the combination of Fr1, (y2-y1)/y1, and W/Z parameters performed more successfully. Also, comparison between KELM and the Artificial Neural Networks (ANN) approach showed that KELM is more successful in the predicting process.

scholarly journals Forecasting Dry Bulk Freight Index with Improved SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Qianqian Han ◽  
Bo Yan ◽  
Guobao Ning ◽  
B. Yu
Keyword(s):  
Wavelet Transform ◽  
Support Vector ◽  
Data Series ◽  
Shipping Industry ◽  
Combined Model ◽  
Final Model ◽  
Proposed Model ◽  
Svm Model ◽  
The Impact ◽  
Short Term Trend

An improved SVM model is presented to forecast dry bulk freight index (BDI) in this paper, which is a powerful tool for operators and investors to manage the market trend and avoid price risking shipping industry. The BDI is influenced by many factors, especially the random incidents in dry bulk market, inducing the difficulty in forecasting of BDI. Therefore, to eliminate the impact of random incidents in dry bulk market, wavelet transform is adopted to denoise the BDI data series. Hence, the combined model of wavelet transform and support vector machine is developed to forecast BDI in this paper. Lastly, the BDI data in 2005 to 2012 are presented to test the proposed model. The 84 prior consecutive monthly BDI data are the inputs of the model, and the last 12 monthly BDI data are the outputs of model. The parameters of the model are optimized by genetic algorithm and the final model is conformed through SVM training. This paper compares the forecasting result of proposed method and three other forecasting methods. The result shows that the proposed method has higher accuracy and could be used to forecast the short-term trend of the BDI.

scholarly journals Experimental Investigation of Flood Energy Dissipation by Single and Hybrid Defense System

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1971
Author(s):  
Afzal Ahmed ◽  
Abdul Razzaq Ghumman
Keyword(s):  
Energy Dissipation ◽  
Energy Loss ◽  
Delay Time ◽  
Hydraulic Jump ◽  
Average Energy ◽  
Defense System ◽  
Maximum Delay ◽  
Different Shapes ◽  
The Impact ◽  
Better Than

In this study, a series of laboratory experiments were conducted to investigate the energy loss through the hybrid defense system (HDS) in the order of dike, moat, and emergent vegetation in steady subcritical flow conditions. The results of HDS were compared with a single defense system (SDS) comprising only vegetation (OV). The dimensions of dike were kept constant while two different shapes (trapezoidal and rectangular) of moat were considered. The impacts of vegetation of variable thickness and density were investigated. Two combinations of HDS were investigated including the combination of dike and vegetation (DV) and the combination of dike, moat, and vegetation (DMV). The effect of backwater rise due to the vegetation, hydraulic jump formation and the impact of the arrival time of floodwater on energy dissipation were investigated. It was observed that on the upstream side of obstructions, the backwater depth increased by increasing the Froude number in both the SDS and HDS. The hydraulic jump observed in HDS was classified and the energy dissipation due to it was calculated. Under various conditions investigated in this paper, the maximum average energy dissipation was 32% in SDS and 46% in HDS. The trapezoidal moat performed better than rectangular moat as energy dissipater. The delay time was also greater with trapezoidal moat as compared to that in rectangular one. The maximum delay time was 140 s in the case of HDS. Hence, the hybrid defense system offered maximum resistance to the flow of water, thus causing a significant energy loss. For each case of SDS and HDS, empirical equations were developed by regression analysis to estimate the energy dissipation amounts.

Experimental Study of Sand Sediments Effects on Submerged Hydraulic Jump Properties

2012 ◽  
Vol 212-213 ◽  
pp. 366-371
Author(s):  
Siavash Haghighi ◽  
Mohammad Reza Kavianpour ◽  
Keyvan Nasiri
Keyword(s):  
Energy Dissipation ◽  
Hydraulic Jump ◽  
Suspended Sediment Concentration ◽  
Sediment Load ◽  
Sediment Concentration ◽  
Relative Energy ◽  
Sluice Gate ◽  
Jump Length ◽  
Submerged Hydraulic Jump ◽  
Whole Sediments

Abstract. In this study, the effect of sediment concentration on submerged hydraulic jump (SHJ) characteristics such as jump length, submerged depth on the gate and the energy dissipation is investigated. Experiments were carried out in a flume of 46 cm depth, 12 m length. The width of the flume changes from 10 cm (at the entrance) to 60 cm (at the exit). Sediment load and flow concentration have an influence on submerged hydraulic jump characteristics including submerged depth on the gate, jump length and relative energy dissipation. It is shown that at high Froude numbers increasing the suspended sediment concentration to 28.7 gr/l leads to a reduction in the submerged depth on the gate up to 6% and jump length up to 10%. Also, the energy dissipation of the submerged hydraulic jump increases by 4% and turbulence resulting from the jump leads to upright distribution of concentration at the end of the jump. Also in concentrations higher than 30 gr/l, flow is not able to carry the whole sediments and subsequently leads to their deposition in subcritical area and behind the sluice gate.

Information Visualization Using Clustering and Predictive Model

2020 ◽  
Vol 11 (4) ◽  
pp. 24-36
Author(s):  
Manojit Chattopadhyay ◽  
Debdatta Pal
Keyword(s):  
Predictive Model ◽  
Predictor Variables ◽  
Support Vector ◽  
Data Series ◽  
Significant Variable ◽  
Prediction Errors ◽  
Self Organizing Map ◽  
Self Organising Map ◽  
Inventory Holding ◽  
The Impact

This paper aims to reveal the impact of rainfall on tea export from India, an issue that remained unexplored in the existing literature. This study explores a new model to predict India's tea export more accurately that would be helpful for Indian tea planters and exporters to plan their production as well as the inventory holding for deriving maximum value from tea export. A two-stage modelling approach has been developed. Firstly, an artificial intelligence-based growing hierarchical self-organising map algorithm is employed on the monthly time series of monthly frequency spreading over April 2005 to December 2013 to segregate India's monthly tea export data series into visual clusters of recognized pattern. Further, a predictive model using support vector machine has been developed and applied to forecast the tea export and then the importance of the predictor variables of the tea export have been identified. Finally, using the appropriate measures of performance a comparative analysis has been performed for each of the model. The newness of the study pertains to the two facts revealed from the study: firstly, India's tea export is embedded of complexity and nonlinearity, which could receive a successful clustering through growing hierarchical self organizing map that would make a deeper analysis easier with a further application of rich statistical techniques. Secondly, the analysis of prediction errors and the relative importance of the predictor variables establish rainfall as one of the most significant variable in predicting India's tea export, insight that has never surfaced in the literature developed thus far.

scholarly journals Artificial-Intelligence-Based Time-Series Intervention Models to Assess the Impact of the COVID-19 Pandemic on Tomato Supply and Prices in Hyderabad, India

Agronomy ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1878
Author(s):  
Gayathri Chitikela ◽  
Meena Admala ◽  
Vijaya Kumari Ramalingareddy ◽  
Nirmala Bandumula ◽  
Gabrijel Ondrasek ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Negative Impact ◽  
Superior Performance ◽  
Support Vector ◽  
Complex Nature ◽  
Time Series Models ◽  
Intervention Model ◽  
Intervention Models ◽  
The Impact

This study’s objective was to assess the impact of the COVID-19 pandemic on tomato supply and prices in Gudimalkapur market in Hyderabad, India. The lockdown imposed by the government of India from 25 March 2020 to 30 June 2020 particularly affected the supply chain of perishable agricultural products, including tomatoes as one of the major vegetable crops in the study area. The classical time series models such as autoregressive integrated moving average (ARIMA) intervention models and artificial intelligence (AI)-based time-series models namely support vector regression (SVR) intervention and artificial neural network (ANN) intervention models were used to predict tomato supplies and prices in the studied market. The modelling results show that the pandemic had a negative impact on supply and a positive impact on tomato prices. Moreover, the ANN intervention model outperformed the other models in both the training and test data sets. The superior performance of the ANN intervention model could be due to its ability to account for the nonlinear and complex nature of the data with exogenous intervention variable.

scholarly journals The impact of feature extraction and selection for the classification of gait patterns between ACL deficient and intact knees based on different classification models

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wei Zeng ◽  
Shiek Abdullah Ismail ◽  
Evangelos Pappas
Keyword(s):  
Feature Extraction ◽  
Superior Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Classification Models ◽  
Acl Deficiency ◽  
Feature Extraction And Selection ◽  
Dynamical Features ◽  
The Impact ◽  
Acl Deficient

AbstractThe anterior cruciate ligament (ACL) plays an important role in stabilizing translation and rotation of the tibia relative to the femur. Individuals with ACL deficiency usually demonstrate alterations in gait characteristics. Evidence indicates that walking speed, alterations in kinetics and kinematics on the ACL deficient limb, and inter-limb asymmetries between deficient and intact knees may contribute to poor long-term outcomes following ACL deficiency. They corrode function of the knee joint and put it at higher risk of degeneration. For the purpose of developing an automatic and highly accurate system for detection of ACL deficiency, this study investigated the classification capability of different dynamical features extracted from gait kinematic and kinetic signals when evaluating their impact on different classification models. A general feature extraction framework was proposed and various dynamical features, such as recurrence rate, determinism and entropy from the recurrence quantification analysis, fuzzy entropy, Teager-Kaiser energy feature and statistical analysis, were included. Different classification models, including support vector machine (SVM), K-nearest neighbor (KNN), naive Bayes (NB) classifier, decision tree (DT) classifier and ensemble learning based Adaboost (ELA) classifier, derived for discriminant analysis of multiple dynamical gait features were evaluated for a comparative study. The effectiveness of this strategy was verified using a dataset of knee, hip and ankle kinematic and kinetic waveforms from 43 patients with unilateral ACL deficiency. When evaluated with 2-fold, 10-fold and leave-one-out cross-validation styles, the highest classification accuracy for discriminating between groups of ACL deficient and contralateral ACL intact knees was reported to be 91.22 $$\%$$ % , 95.12$$\%$$ % and 96.34$$\%$$ % , respectively,by using the SVM classifier and the optimal feature set. For other four classifiers, KNN achieved the accuracy of 78.05$$\%$$ % , 85.37$$\%$$ % and 87.80$$\%$$ % , respectively. NB achieved the accuracy of 57.56$$\%$$ % , 60.98$$\%$$ % and 61.22$$\%$$ % , respectively. DT achieved the accuracy of 77.56$$\%$$ % , 80.49$$\%$$ % and 83.66$$\%$$ % , respectively. ELA achieved the accuracy of 73.66$$\%$$ % , 78.05$$\%$$ % and 79.27$$\%$$ % , respectively. Compared with other state-of-the-art methods, the results demonstrate superior performance and support the validity of the proposed method.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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