Evolutionary design of generalized group method of data handling-type neural network for estimating the hydraulic jump roller length

Azimi, Hamed; Bonakdari, Hossein; Ebtehaj, Isa; Gharabaghi, Bahram; Khoshbin, Fatemeh

doi:10.1007/s00707-017-2043-9

Cited by 71 publications

(11 citation statements)

References 43 publications

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“…To investigate the alteration trend of the scour depth around abutment to each input variable provided in the Equation (20) (F e , h/L, d 50 /L, k s ), the partial derivative sensitivity analysis (PDSA) was performed [73,74]. In the PDSA, the partial differential of the development model was calculated between output and each input variable, and the results of the partial difference were reported as the sensitivity of the developed model to x i input (in the current study, I = 1, 2, 3, 4).…”

Section: Discussionmentioning

confidence: 99%

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Bonakdari

Moradi

Ebtehaj

et al. 2020

Water

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Abutment scour is a complex three-dimensional phenomenon, which is one of the leading causes of marine structure damage. Structural integrity is potentially attainable through the precise estimation of local scour depth. Due to the high complexity of scouring hydrodynamics, existing regression-based relations cannot make accurate predictions. Therefore, this study presented a novel expansion of extreme learning machines (ELM) to predict abutment scour depth (ds) in clear water conditions. The model was built using the relative flow depth (h/L), excess abutment Froude number (Fe), abutment shape factor (Ks), and relative sediment size (d50/L). A wide range of experimental samples was collected from the literature, and data was utilized to develop the ELM model. The ELM model reliability was evaluated based on the estimation results and several statistical indices. According to the results, the sigmoid activation function (correlation coefficient, R = 0.97; root mean square error, RMSE = 0.162; mean absolute percentage error, MAPE = 7.69; and scatter index, SI = 0.088) performed the best compared with the hard limit, triangular bias, radial basis, and sine activation functions. Eleven input combinations were considered to investigate the impact of each dimensionless variable on the abutment scour depth. It was found that ds/L = f (Fe, h/L, d50/L, Ks) was the best ELM model, indicating that the dimensional analysis of the original data properly reflected the underlying physics of the problem. Also, the absence of one variable from this input combination resulted in a significant accuracy reduction. The results also demonstrated that the proposed ELM model significantly outperformed the regression-based equations derived from the literature. The ELM model presented a fundamental equation for abutment scours depth prediction. Based on the simulation results, it appeared the ELM model could be used effectively in practical engineering applications of predicting abutment scour depth. The estimated uncertainty of the developed ELM model was calculated and compared with the conventional and artificial intelligence-based models. The lowest uncertainty with a value of ±0.026 was found in the proposed model in comparison with ±0.50 as the best uncertainty of the other models.

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Section: Discussionmentioning

confidence: 99%

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Bonakdari

Moradi

Ebtehaj

et al. 2020

Water

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“…In this section, the performance of the WSAELM models in simulating the runoff–precipitation amounts is evaluated through an uncertainty analysis. Generally, uncertainty analysis is a useful tool for assessing the performance of numerical models (Karbasi and Azamathulla, 2016; Azimi et al, 2018, , 2019). In other words, uncertainty analysis is performed to measure the error predicted by numerical models and evaluate their performance.…”

Section: Resultsmentioning

confidence: 99%

“…There are various statistical indices for evaluating results of numerical models. Firstly, the applied criteria are universally valid and have been extensively applied in different numerical studies (Ebtehaj et al, 2015; Azimi et al, 2018; Malekzadeh et al, 2019). Secondly, different criteria should be used to evaluate numerical models appropriately because applied statistical indices have different acceptable ranges.…”

Section: Methodsmentioning

confidence: 99%

Simulation of rainfall and runoff time series using robust machine learning*

Alizadeh

Rajabi

Shabanlou

et al. 2020

Irrigation and Drainage

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In this paper, the precipitation and runoff time‐series data of the Shaharchay River basin from 2000 to 2017 are simulated by a modern hybrid artificial intelligence technique. In order to develop the mentioned artificial intelligence model, the extreme learning machine (ELM), differential evolution and wavelet transform are combined and the SAELM and WASAELM hybrid models are provided. Initially, the most effective lags of the time‐series data are distinguished using an autocorrelation function. Using the lags, seven artificial intelligence models are defined for each of the SAELM and WSAELM models. To simulate precipitation and runoff, the sym and coif mother wavelets are chosen as the optimal ones, respectively. For the best model, the values of R2, the scatter index and the Nash–Sutcliffe efficiency coefficient (NSC) for simulating precipitation yielded 0.967, 0.208 and 0.965, respectively. Furthermore, a sensitivity analysis shows that the lags (t‐1), (t‐2) and (t‐12) are regarded as the most effective input lags. Ultimately, an uncertainty analysis is carried out for the superior model that the performance of this model in simulating precipitation and runoff is over‐ and underestimated, respectively.

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“…The group method and data handling (GMDH) approach was invented by Ivakhnenko [26] for mathematical modelling of multivariate complex systems [27]. The GMDH model is reliant on efficacy with multi-input and single-output data sets based on reference polynomial functions [28].…”

Section: Group Methods and Data Handling Neural Network (Gmdhnn)mentioning

confidence: 99%

Air temperature prediction using different machine learning models

Adnan

Liang

Kuriqi

et al. 2021

IJEECS

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Air temperature is an essential climatic component particularly in water resources management and other agro-hydrological/meteorological activities planning This paper examines the prediction capability of three machine learning models, least square support vector machine (LSSVM), group method and data handling neural network (GMDHNN) and classification and regression trees (CART) in air temperature forecasting using monthly temperature data of Astore and Gilgit climatic stations of Pakistan. The prediction capability of three machine learning models is evaluated using different time lags input combinations with help of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R<sup>2</sup>).statistical indicators. The obtained results indicated that the LSSVM model is more accurate in temperature forecasting than GMDHNN and CART models. LSSVM significantly decreases the mean RMSE of the GMHNN and CART models by 1.47-3.12% and 20.01-25.12% for the Chakdara and Kalam Stations, respectively.

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Evolutionary design of generalized group method of data handling-type neural network for estimating the hydraulic jump roller length

Cited by 71 publications

References 43 publications

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Simulation of rainfall and runoff time series using robust machine learning*

Air temperature prediction using different machine learning models

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