Discussion of “Gene-Expression Programming, Evolutionary Polynomial Regression, and Model Tree to Evaluate Local Scour Depth at Culvert Outlets” by Mohammad Najafzadeh and Ali Reza Kargar

2021 ◽  
Vol 12 (2) ◽  
pp. 07021001
Author(s):  
Manish Pandey ◽  
H. Md Azamathulla
Keyword(s):  
Gene Expression ◽  
Polynomial Regression ◽  
Gene Expression Programming ◽  
Local Scour ◽  
Scour Depth ◽  
Evolutionary Polynomial Regression ◽  
Model Tree

scholarly journals Gene-expression programming to predict the local scour depth at downstream of stilling basins

2016 ◽  
Vol 23 (1) ◽  
pp. 102-113 ◽  
Author(s):  
M. Mesbahi ◽  
N. Talebbeydokhti ◽  
S.-A. Hosseini ◽  
S.-H. Afzali
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Local Scour ◽  
Scour Depth ◽  
Stilling Basins

scholarly journals Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models

2016 ◽  
Vol 18 (5) ◽  
pp. 867-884 ◽  
Author(s):  
Mohammad Najafzadeh ◽  
Mohammad Rezaie Balf ◽  
Esmat Rashedi
Keyword(s):  
Polynomial Regression ◽  
Gene Expression Programming ◽  
Laboratory Data ◽  
Experimental Studies ◽  
Local Scour ◽  
Bridge Piers ◽  
Scour Depth ◽  
Effective Parameters ◽  
Pier Scour ◽  
Debris Accumulation

Pier scour phenomena in the presence of debris accumulation have attracted the attention of engineers to present a precise prediction of the local scour depth. Most experimental studies of pier scour depth with debris accumulation have been performed to find an accurate formula to predict the local scour depth. However, an empirical equation with appropriate capacity of validation is not available to evaluate the local scour depth. In this way, gene-expression programming (GEP), evolutionary polynomial regression (EPR), and model tree (MT) based formulations are used to develop to predict the scour depth around bridge piers with debris effects. Laboratory data sets utilized to perform models are collected from different literature. Effective parameters on the local scour depth include geometric characterizations of bridge piers and debris, physical properties of bed sediment, and approaching flow characteristics. The efficiency of the training stages for the GEP, MT, and EPR models are investigated. Performances of the testing results for these models are compared with the traditional approaches based on regression methods. The uncertainty prediction of the MT was quantified and compared with those of existing models. Also, sensitivity analysis was performed to assign effective parameters on the scour depth prediction.

scholarly journals A Comparative Study on the Performance of Gene Expression Programming and Machine Learning Methods

2021 ◽  
pp. 140-147
Author(s):  
Mohammad Anas ◽  
Mohiuddeen Khan ◽  
Hammad Basit
Keyword(s):  
Gene Expression ◽  
Performance Metrics ◽  
Polynomial Regression ◽  
Gene Expression Programming ◽  
Research Work ◽  
Living Organism ◽  
Computer Programs ◽  
Test Accuracy ◽  
Tree Structures ◽  
High Bias

Usually, evolutionary algorithms are used to provide strong approximations to problems that are difficult to solve with other methods. Gene expression programming (GEP) is a type of evolutionary algorithm used in computer programming to generate computer programs or models. These computer programs are complex tree structures that, like a living organism, learn and adapt by modifying their sizes, shapes, and composition. In the present work, a comparison study was made among GEP and the standard prediction techniques to find the best predicting model on the BOSTON HOUSING dataset. Three approaches viz. GEP, ANN and polynomial regression were implemented on the dataset. The study showed how the three methods solve the problem of high bias and high variance and which one outperforms the other. The research work, however, gave a glimpse of the actual limitations and advantages of the methods on one another indicating the dependency of method on the type of data used. The results conclude the comparison of different methods on different performance metrics. The GEP model however reduced the problem of high bias and high variance by giving a slight difference between the train and test accuracy but was not able to outperform ANN and polynomial regression in terms of performance metrics.

scholarly journals Gene-expression programming to predict scour at a bridge abutment

2011 ◽  
Vol 14 (2) ◽  
pp. 324-331 ◽  
Author(s):  
H. Md. Azamathulla
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Gene Expression Programming ◽  
Accurate Estimation ◽  
Scour Depth ◽  
Laboratory Measurements ◽  
Ann Modeling ◽  
Bridge Abutment ◽  
Alternative Approach ◽  
Artificial Neural Network Ann

The process involved in the local scour at an abutment is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This study presents the use of gene-expression programming (GEP), which is an extension of genetic programming (GP), as an alternative approach to estimate the scour depth. The datasets of laboratory measurements were collected from the published literature and used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in training. The proposed GEP approach gives satisfactory results compared with existing predictors and artificial neural network (ANN) modeling in predicting the scour depth at an abutment.

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