scholarly journals Explicit prediction of expanding channels hydraulic jump characteristics using gene expression programming approach

2017 ◽  
Vol 49 (3) ◽  
pp. 815-830 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Roghayeh Ghasempour
Keyword(s):  
Gene Expression ◽  
Hydraulic Jump ◽  
Gene Expression Programming ◽  
Relative Energy ◽  
Expansion Ratio ◽  
Programming Approach ◽  
Jump Length ◽  
Input Parameters ◽  
Sequent Depth ◽  
Better Than

Abstract Hydraulic jump is a useful means of dissipating excess energy of a supercritical flow so that objectionable scour downstream is minimized. The present study applies gene expression programming (GEP) to estimate hydraulic jump characteristics in sudden expanding channels. Three types of expanding channels were considered: channels without appurtenances, with a central sill, and with a negative step. 1,000 experimental data were considered as input data to develop models. The results proved the capability of GEP in predicting hydraulic jump characteristics in expanding channels. It was found that the developed models for channel with a central sill performed better than other channels. In the jump length prediction, the model with input parameters Fr1 and (y2—y1)/y1, and in the sequent depth ratio and relative energy dissipation prediction the model with input parameters Fr1 and y1/B led to more accurate outcomes (Fr1, y1, y2, and B are Froude number, sequent depth of upstream and downstream, and expansion ratio, respectively). Sensitivity analysis showed that Fr1 had the key role in modeling. The GEP models were compared with existing empirical equations and it was found that the GEP models yielded better results. It was also observed that channel and appurtenances geometry affected the modeling.

scholarly journals Experimental study and modeling of hydraulic jump for a suddenly expanding stilling basin using different hybrid algorithms

2021 ◽  
Author(s):  
Enes Gul ◽  
O. Faruk Dursun ◽  
Abdolmajid Mohammadian
Keyword(s):  
Experimental Study ◽  
Hydraulic Jump ◽  
Imperialist Competitive Algorithm ◽  
Expansion Ratio ◽  
Stilling Basin ◽  
Jump Length ◽  
Depth Ratio ◽  
Input Variables ◽  
Sequent Depth ◽  
Learning Machine

Abstract Hydraulic jump is a highly important phenomenon for dissipation of energy. This event, which involves flow regime change, can occur in many different types of stilling basins. In this study, hydraulic jump characteristics such as relative jump length and sequent depth ratio occurring in a suddenly expanding stilling basin were estimated using hybrid Extreme Learning Machine (ELM). To hybridize ELM, Imperialist Competitive Algorithm (ICA), Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) metaheuristic algorithms were implemented. In addition, six different models were established to determine effective dimensionless (relative) input variables. A new dataset was constructed by adding the data obtained from the experimental study in the present research to the data obtained from the literature. The performance of each model was evaluated using k-fold cross validation. Results showed that ICA hybridization slightly outperformed FA and PSO methods. Considering relative input parameters, Froude number (Fr), expansion ratio (B) and relative sill height (S), and effective input combinations were Fr – B– S and Fr – B for the prediction of the sequent depth ratio (Y) and relative hydraulic jump length (Lj/h1), respectively.

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.

Forecasting Volatility Returns of Oil Price Using Gene Expression Programming Approach

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
Alexander Amo Baffour ◽  
Jingchun Feng ◽  
Liwei Fan ◽  
Beryl Adormaa Buanya
Keyword(s):  
Gene Expression ◽  
Moving Average ◽  
Gene Expression Programming ◽  
Price Volatility ◽  
Oil Price ◽  
Loss Functions ◽  
Programming Approach ◽  
Volatility Forecasting ◽  
Oil Price Volatility ◽  
Forecasting Volatility

AbstractThis study employs four (4) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) variants namely GARCH (1, 1), Glosten–Jagannathan–Runkle (GJR), Auto Regressive Integrated Moving Average (ARIMA)-GARCH and ARIMA-GJR as benchmark models to assess the performance of a proposed novel Gene Expression Programming (GEP) based univariate time series modeling approach used to conduct ex ante oil price volatility forecasts. The report illustrates that the GEP model is more superior to any of the traditional models on issues relating to both loss functions applied. The GEP model is of a greater volatility forecasting precision at different forecast horizons, therefore. There is also the existence of evidence that GJR and ARIMA-GJR differ in their loss functions, the performance is nevertheless better than GARCH (1, 1) and ARIMA-GARCH. This study conducted herein achieves importance in literature by broadening the application of gene algorithms in finance and forecasting. It also solves the problem of high error associated with the use of GARCH related models in oil price volatility forecasting.

A Gene Expression Programming Approach for Vehicle Body Segmentation and Color Recognition

2020 ◽  
Author(s):  
Brenda Cinthya Solari Berno ◽  
Lucas Augusto Albini ◽  
Vinícius Couto Tasso ◽  
César Manuel Vargas Benítez ◽  
Heitor Silvério Lopes
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Vehicle Body ◽  
Programming Approach ◽  
Color Recognition ◽  
Body Segmentation
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