Prediction of hydro-suction dredging depth using data-driven methods

Mahdavi‐Meymand, Amin; Zounemat‐Kermani, Mohammad; Qaderi, Kourosh

doi:10.1007/s11709-021-0719-7

Cited by 5 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mahdavi-Meymand et al applied different kinds of GMDH that integrated with PSO and HGSO algorithms to simulate the maximum hydro-suction dredging depth. The results demonstrated that the GMDH-HGSO algorithm provides an excellent fit to the observed data 16 . Ezzaouini et al used RF, adaptive boosting (AdaBoost), SVR, K-NN, and ANN models to predict the suspended sediment load.…”

Section: Introductionmentioning

confidence: 86%

Application of classical and novel integrated machine learning models to predict sediment discharge during free-flow flushing

Javadi

Qaderi

Ahmadi

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

In this study, the capabilities of classical and novel integrated machine learning models were investigated to predict sediment discharge (Qs) in free-flow flushing. Developed models include Multivariate Linear Regression (MLR), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Group Method of Data Handling (GMDH), and four hybrid forms of GMDH and Support Vector Regression (SVR) in combination with Henry Gas Solubility Optimization (HGSO) and Equilibrium Optimizer (EO) algorithms. The number of 160 datasets that were applied to assess these models was extracted from the Janssen (PhD’s Thesis, Engineering, Civil and Environmental Engineering. University of California, 1999) experimental study. Input parameters to predict Qs included the water level in the reservoir (hw), bed level in the flushing channel (hb), outflow (Qout), inflow (Qin), and elapsed time of flushing (T). The performance of all models was evaluated by four statistical indices of root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R2), and Mean absolute relative error (MARE). Evaluation of results demonstrated that the HGSO and EO algorithms could enhance the accuracy of the GMDH model (up to 26% and 22% in terms of RMSE), respectively. According to statistical criteria, the SVR-EO and SVR-HGSO provided the highest accuracy in both training (R2 = 0.98) and validation phases (R2 = 0.96). Moreover, among the developed models, the GMDH-HGSO algorithm provided excellent fitness to the observed data (R2 = 0.96, RMSE = 22.37, MAE = 15.65, and MARE = 0.26). The results indicated the high efficiency of the HGSO and EO algorithms in improving the accuracy of the GMDH and SVR models. However, among the developed models, the GMDH-HGSO is the most accurate model and is recommended for sediment transport modelling.

show abstract

Section: Introductionmentioning

confidence: 86%

Application of classical and novel integrated machine learning models to predict sediment discharge during free-flow flushing

Javadi

Qaderi

Ahmadi

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…The second-order polynomial may be written in the following form: where y is the output, ( x 1 , x 2 ) is the input vector, and c is the weighting coefficient. The intricacy of the neurons will be increased layer by layer, which causes that the final network is becoming complex 67 . The weighting coefficients are calculated using regression techniques: where c represents the weighting coefficient vector, A denotes the following matrix: and Y is the matrix of outputs: in which m is the number of samples.…”

Section: Methodsmentioning

confidence: 99%

Modeling of wave run-up by applying integrated models of group method of data handling

Mahdavi‐Meymand

Zounemat‐Kermani

Sulisz

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

Wave-induced inundation in coastal zones is a serious problem for residents. Accurate prediction of wave run-up height is a complex phenomenon in coastal engineering. In this study, several machine learning (ML) models are developed to simulate wave run-up height. The developed methods are based on optimization techniques employing the group method of data handling (GMDH). The invasive weed optimization (IWO), firefly algorithm (FA), teaching–learning-based optimization (TLBO), harmony search (HS), and differential evolution (DE) meta-heuristic optimization algorithms are embedded with the GMDH to yield better feasible optimization. Preliminary results indicate that the developed ML models are robust tools for modeling the wave run-up height. All ML models’ accuracies are higher than empirical relations. The obtained results show that employing heuristic methods enhances the accuracy of the standard GMDH model. As such, the FA, IWO, DE, TLBO, and HS improve the RMSE criterion of the standard GMDH by the rate of 47.5%, 44.7%, 24.1%, 41.1%, and 34.3%, respectively. The GMDH-FA and GMDH-IWO are recommended for applications in coastal engineering.

show abstract

Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data

Adnan

Dai

Mostafa

et al. 2022

Geocarto International

View full text Add to dashboard Cite

Prediction of hydro-suction dredging depth using data-driven methods

Cited by 5 publications

References 28 publications

Application of classical and novel integrated machine learning models to predict sediment discharge during free-flow flushing

Application of classical and novel integrated machine learning models to predict sediment discharge during free-flow flushing

Modeling of wave run-up by applying integrated models of group method of data handling

Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data

Contact Info

Product

Resources

About