A summary of reported studies on modeling hydraulic jump characteristics using soft computing techniques
| Soft computing method(s) . | Author(s)/year . | Parameter(s) . | Objective of the study . | Conclusion remarks . |
|---|---|---|---|---|
| MLPNN | Omid et al. (2005) | Lj, h2 | An artificial neural network (ANN) approach was applied to model sequent depth and jump length | For the rectangular section, the neural network model successfully predicted the jump length as well as the sequent depth values |
| MLPNN | Naseri & Othman (2012) | Lj | In this study, an ANN technique was developed to determine the length of the hydraulic jumps in a rectangular section with a horizontal apron | A comparison between the selected ANN model and the empirical Silvester equation was also made, and the results showed that the ANN method was more precise |
| MLPNN/GP | Abbaspour et al. (2013) | Lj, h2 | ANNs and genetic programming (GP) were used for the estimation of hydraulic jump characteristics | Results showed that the proposed ANN models were much more accurate than the GP models |
| MLPNN/GRNN | Houichi et al. (2013) | Lj | Two different ANNs were implemented to model the relative lengths of hydraulic jumps | The results demonstrated that both the MLPNN and GRNN were reliable predictive tools for simulating the hydraulic jump properties |
| GEP/SVR/MLPNN | Karbasi & Azamathulla (2016) | Lj | Application of several soft computing models to predict characteristics of hydraulic jumps over rough beds | ANN and SVR provided better results than the GEP model |
| ANFIS/ANFIS-FA | Azimi et al. (2018a) | Lr | Evaluating the potential of FA algorithm in simulating the hydraulic jump | Integrating the FA algorithm with ANFIS made the standard ANFIS produce more accurate results |
| GMDH/MLPNN | Azimi et al. (2018b) | Lr | Estimating the roller length of hydraulic jumps on rough beds using GMDH and ANN models | The suggested soft computing models’ predictions were closer to the observed values than a number of other empirical models |
| ANFIS/Differential Evolution | Gerami Moghadam et al. (2019) | Lj | A hybrid method (ANFIS-DE) was proposed for modeling hydraulic jumps on sloping rough beds | Two parameters including the ratio of sequent depths and the Froude number were identified as the most important parameters in modeling the hydraulic jump length |
| GEP | Azimi et al. (2019) | Lr | Prediction of the roller length of a hydraulic jump | A simple and practical equation was proposed for predicting the length of a hydraulic jump |
| MLPNN | Kumar et al. (2019) | h2/h1 | Prediction of sequent depth ratio | MLPNN provided better results than empirical models |
| ELM | Azimi et al. (2020) | Lj | Prediction of hydraulic jump length on slope rough beds | The flow Froude number at upstream was introduced as the most effective parameter in predicting the jump length |
| Soft computing method(s) . | Author(s)/year . | Parameter(s) . | Objective of the study . | Conclusion remarks . |
|---|---|---|---|---|
| MLPNN | Omid et al. (2005) | Lj, h2 | An artificial neural network (ANN) approach was applied to model sequent depth and jump length | For the rectangular section, the neural network model successfully predicted the jump length as well as the sequent depth values |
| MLPNN | Naseri & Othman (2012) | Lj | In this study, an ANN technique was developed to determine the length of the hydraulic jumps in a rectangular section with a horizontal apron | A comparison between the selected ANN model and the empirical Silvester equation was also made, and the results showed that the ANN method was more precise |
| MLPNN/GP | Abbaspour et al. (2013) | Lj, h2 | ANNs and genetic programming (GP) were used for the estimation of hydraulic jump characteristics | Results showed that the proposed ANN models were much more accurate than the GP models |
| MLPNN/GRNN | Houichi et al. (2013) | Lj | Two different ANNs were implemented to model the relative lengths of hydraulic jumps | The results demonstrated that both the MLPNN and GRNN were reliable predictive tools for simulating the hydraulic jump properties |
| GEP/SVR/MLPNN | Karbasi & Azamathulla (2016) | Lj | Application of several soft computing models to predict characteristics of hydraulic jumps over rough beds | ANN and SVR provided better results than the GEP model |
| ANFIS/ANFIS-FA | Azimi et al. (2018a) | Lr | Evaluating the potential of FA algorithm in simulating the hydraulic jump | Integrating the FA algorithm with ANFIS made the standard ANFIS produce more accurate results |
| GMDH/MLPNN | Azimi et al. (2018b) | Lr | Estimating the roller length of hydraulic jumps on rough beds using GMDH and ANN models | The suggested soft computing models’ predictions were closer to the observed values than a number of other empirical models |
| ANFIS/Differential Evolution | Gerami Moghadam et al. (2019) | Lj | A hybrid method (ANFIS-DE) was proposed for modeling hydraulic jumps on sloping rough beds | Two parameters including the ratio of sequent depths and the Froude number were identified as the most important parameters in modeling the hydraulic jump length |
| GEP | Azimi et al. (2019) | Lr | Prediction of the roller length of a hydraulic jump | A simple and practical equation was proposed for predicting the length of a hydraulic jump |
| MLPNN | Kumar et al. (2019) | h2/h1 | Prediction of sequent depth ratio | MLPNN provided better results than empirical models |
| ELM | Azimi et al. (2020) | Lj | Prediction of hydraulic jump length on slope rough beds | The flow Froude number at upstream was introduced as the most effective parameter in predicting the jump length |
MLPNN: multi-layer perceptron neural network; GP: genetic programming; GEP: Gene expression programming; SVR: support vector regression; GMDH: group method of data handling; ANFIS: adaptive neuro-fuzzy inference system; ELM: Extreme Learning Machine.