Sediment transport modeling in open channels using neuro-fuzzy and gene expression programming techniques

2019 ◽  
Vol 79 (12) ◽  
pp. 2318-2327 ◽  
Author(s):  
Katayoun Kargar ◽  
Mir Jafar Sadegh Safari ◽  
Mirali Mohammadi ◽  
Saeed Samadianfard
Keyword(s):  
Gene Expression ◽  
Sediment Transport ◽  
Gene Expression Programming ◽  
Technical Problem ◽  
Deposition Condition ◽  
Regression Equations ◽  
Rigid Boundary ◽  
Channel Characteristics ◽  
Neuro Fuzzy ◽  
Programming Techniques

Abstract Deposition of sediment is a vital economical and technical problem for design of sewers, urban drainage, irrigation channels and, in general, rigid boundary channels. In order to confine continuous sediment deposition, rigid boundary channels are designed based on self-cleansing criteria. Recently, instead of using a single velocity value for design of the self-cleansing channels, more hydraulic parameters such as sediment, fluid, flow and channel characteristics are being utilized. In this study, two techniques of neuro-fuzzy (NF) and gene expression programming (GEP) are implemented for particle Froude number estimation of the non-deposition condition of sediment transport in rigid boundary channels. The models are established based on laboratory experimental data with wide ranges of sediment and pipe sizes. The developed models' performances have been compared with empirical equations based on two statistical factors comprising the root mean square error (RMSE) and the concordance coefficient (CC). Besides, Taylor diagrams are used to test the resemblance between measured and calculated values. The outcomes disclose that NF4, as the precise NF model, performs better than the best GEP model (GEP1) and regression equations. As a conclusion, the obtained results proved the suitable accuracy and applicability of the NF method in estimation.

Reliable method of determining stable threshold channel shape using experimental and gene expression programming techniques

2018 ◽  
Vol 31 (10) ◽  
pp. 5799-5817 ◽  
Author(s):  
Azadeh Gholami ◽  
Hossein Bonakdari ◽  
Mohammad Zeynoddin ◽  
Isa Ebtehaj ◽  
Bahram Gharabaghi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Reliable Method ◽  
Gene Expression Programming ◽  
Channel Shape ◽  
Threshold Channel ◽  
Programming Techniques

Suspended sediment transport in rigid boundary channels at limit deposition

1998 ◽  
Vol 37 (1) ◽  
pp. 147-154 ◽  
Author(s):  
Chandramouli Nalluri ◽  
Fabio Spaliviero
Keyword(s):  
Experimental Data ◽  
Sediment Transport ◽  
Suspended Sediment ◽  
Technical Problem ◽  
Friction Model ◽  
Suspended Sediment Transport ◽  
Rigid Boundary ◽  
Sediment Size ◽  
Wide Range ◽  
The University

Sedimentation or deposition of sediments is a crucial economical and technical problem for the design of conveyances carrying sediment laden flow such as sewers, irrigation canals and, in general, rigid boundary channels. In light of investigations on sediment transport at the limit of deposition carried out during the last two decades at the University of Newcastle upon Tyne, experimental data on suspended sediment transport collected by Pulliah (1978), Macke (1982) and Arora (1983) are analysed. The data cover a wide range of volumetric concentrations (3.7 to 48542 ppm) and sediment size (0.006 to 0.37 mm). A new model for the prediction of suspended sediment transport in rigid boundary channels at limit deposit is proposed. The model was fitted by multiple regression analysis to Macke's (1982) and Arora's (1983) experimental data. Pulliah's (1978) data validate the relation. Nalluri et al. (1994) bed load friction model is checked with available data and a good agreement is observed.

scholarly journals Application of Gene Expression Programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1106 ◽  
Author(s):  
Mohsin Ali Ali Khan ◽  
Adeel Zafar ◽  
Arslan Akbar ◽  
Muhammad Faisal Javed ◽  
Amir Mosavi
Keyword(s):  
Gene Expression ◽  
Compressive Strength ◽  
Empirical Equation ◽  
Gene Expression Programming ◽  
Curing Temperature ◽  
Fine Aggregate ◽  
Geopolymer Concrete ◽  
Regression Equations ◽  
Soft Computing Techniques ◽  
Water Ratio

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength fc′ of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 fc′ experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (%EW), the percentage of plasticizer (%P), the initial curing temperature (T), the age of the specimen (A), the curing duration (t), the fine aggregate to total aggregate ratio (F/AG), the percentage of total aggregate by volume ( %AG), the percent SiO2 solids to water ratio (% S/W) in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (M), the activator or alkali to FA ratio (AL/FA), the sodium oxide (Na2O) to water ratio (N/W) for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (Ns/No). A GEP empirical equation is proposed to estimate the fc′ of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.

scholarly journals Comparison of neural networks and neuro-fuzzy computing techniques for prediction of peak breach outflow

2016 ◽  
Vol 18 (4) ◽  
pp. 724-740 ◽  
Author(s):  
Hasan G. Elmazoghi ◽  
Vail Karakale (Waiel Mowrtage) ◽  
Lubna S. Bentaher
Keyword(s):  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Data Set ◽  
Inference System ◽  
Embankment Dams ◽  
Small Dams ◽  
Neuro Fuzzy

Accurate prediction of peak outflows from breached embankment dams is a key parameter in dam risk assessment. In this study, efficient models were developed to predict peak breach outflows utilizing artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Historical data from 93 embankment dam failures were used to train and evaluate the applicability of these models. Two scenarios were applied with each model by either considering the whole data set without classification or classifying the set into small dams (48 dams) and large dams (45 dams). In this way, nine models were developed and their results were compared to each other and to the results of the best available regression equations and recent gene expression programming. Among the different models, the ANFIS model of the first scenario exhibited better performance based on its higher efficiency (E = 0.98), higher coefficient of determination (R2 = 0.98) and lower mean absolute error (MAE = 840.9). Moreover, models based on classified data enhanced the prediction of peak outflows particularly for small dams. Finally, this study indicated the potential of the developed ANFIS and ANN models to be used as predictive tools of peak outflow rates of embankment dams.

Yarn Strength Modelling Using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gene Expression Programming (GEP)

2013 ◽  
Vol 8 (4) ◽  
pp. 155892501300800 ◽  
Author(s):  
A.R. Fallahpour ◽  
A.R. Moghassem
Keyword(s):  
Gene Expression ◽  
Root Mean Square Error ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Breaking Strength ◽  
Gene Expression Programming ◽  
Mean Square ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy

This study compares capabilities of two different modelling methodologies for predicting breaking strength of rotor spun yarns. Forty eight yarn samples were produced considering variations in three drawing frame parameters namely break draft, delivery speed, and distance between back and middle rolls. Several topologies with different architectures were trained to get the best adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) models. Prediction performance of the GEP model was compared with that of ANFIS using root mean square error (RMSE) and correlation coefficient (R2-Value) parameters on the test data. Results show that, the GEP model has a significant priority over the ANFIS model in term of prediction accuracy. The correlation coefficient (R2-value) and root mean square error for the GEP model were 0.87 and 0.35 respectively, while these parameters were 0.48 and 0.53 for the ANFIS model. Also, a mathematical formula was developed with high degree of accuracy using GEP algorithm to predict the breaking strength of the yarns. This advantage is not accessible in the ANFIS model.

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