Prediction of water flows in Colorado River, Argentina

Pierini, Jorge Omar; Gómez, Eduardo A.; Telesca, Luciano

doi:10.3856/vol40-issue4-fulltext-5

Cited by 20 publications

(4 citation statements)

References 38 publications

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“…Researchers often use RBF as a soft computing method in engineering simulations [76,77]. Three layers make up an RBF method: input, hidden, and output layers.…”

Section: Hybrid Radial Basis Function Neural Network (Fho à Rbf)mentioning

confidence: 99%

Retraction: Jin, J, Wu, T, Zhang, Z, Fan, J, Lee, X. Application of optimization‐based estimation analysis for predicting the compressive strength of eco‐friendly modified geopolymer concrete. Structural Concrete. 2022. https://doi.org/10.1002/suco.202201051

Jian-qing

Wu²,

Zhang

et al. 2022

Structural Concrete

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As one of the significant generators of greenhouse gases, the construction sector is under tremendous pressure due to the rising concern about global climate change and its detrimental effects on communities. Due to the environmental problems connected to the manufacture of cement, geopolymer concrete () has become a viable option for building materials. The results of this research assist in creating machine learning approaches for predicting the qualities of eco‐friendly concrete, which may be used as an alternative to conventional concrete to help lower emissions in the construction sector. In the current work, three hybrid techniques are used to create predictive models that estimate the compressive strength of when ground granulated blast‐furnace slag () is replaced with natural zeolite (), silica fume (), and various concentrations of . For this purpose, three machine learning methods were considered, named , , and hybridized with Fire Hawk Optimization (). The findings show that all approaches have excellent performance in projecting the of , both in terms of the permitted correlation between observed and predicted values. In conclusion, according to the results provided considering statistical indices, error distribution, and Taylor diagram analysis, it could be resulted that the proposed could be recognized as the outperformed model, where other algorithms were also trustable in prediction procedure of of .

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“…Researchers often use RBF as a soft computing method in engineering simulations [76,77]. Three layers make up an RBF method: input, hidden, and output layers.…”

Section: Hybrid Radial Basis Function Neural Network (Fho à Rbf)mentioning

confidence: 99%

Retraction: Jin, J, Wu, T, Zhang, Z, Fan, J, Lee, X. Application of optimization‐based estimation analysis for predicting the compressive strength of eco‐friendly modified geopolymer concrete. Structural Concrete. 2022. https://doi.org/10.1002/suco.202201051

Jian-qing

Wu²,

Zhang

et al. 2022

Structural Concrete

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“…The sigmoid transfer function is often used as the transfer function of the hidden layers (Zadeh et al 2010;Emiroglu et al 2011;Pierini et al 2012). The sigmoid function is defined as any function that is bounded and has a direct relation between the input variable and the output variable (Smith 1993).…”

Section: Genetic Algorithm-based Artificial Neural Networkmentioning

confidence: 99%

Using two soft computing methods to predict wall and bed shear stress in smooth rectangular channels

2017

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Two soft computing methods were extended in order to predict the mean wall and bed shear stress in open channels. The genetic programming (GP) and Genetic Algorithm Artificial Neural Network (GAA) were investigated to determine the accuracy of these models in estimating wall and bed shear stress. The GP and GAA model results were compared in terms of testing dataset in order to find the best model. In modeling both bed and wall shear stress, the GP model performed better with RMSE of 0.0264 and 0.0185, respectively. Then both proposed models were compared with equations for rectangular open channels, trapezoidal channels and ducts. According to the results, the proposed models performed the best in predicting wall and bed shear stress in smooth rectangular channels. The obtained equation for rectangular channels could estimate values closer to experimental data, but the equations for ducts had poor, inaccurate results in predicting wall and bed shear stress. The equation presented for trapezoidal channels did not have acceptable accuracy in predicting wall and bed shear stress either.

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“…Musarat et al used ARIMA analysis and prediction model to forecast river discharge [27]. Pierini verified ANN's excellent performance in predicting or supplementing daily flow [28]. Xu W successfully applied long short-term memory (LSTM) to the prediction of 10-day average flow and daily average flow [29].…”

Section: Introductionmentioning

confidence: 99%

Overflow Capacity Prediction of Pumping Station Based on Data Drive

Guo¹,

Yan²,

Chen³

et al. 2023

Water

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In recent years, the information requirements of pumping stations have become higher and higher. The prediction of overflow capacity can provide important reference for flood carrying capacity, water resource scheduling and water safety. In order to improve the accuracy, stability and generalization ability of the model, a BiGRU–ARIMA data-driven method based on self-attention mechanism is proposed to predict the flow capacity of the pump station. Bidirectional gated recurrent unit (BiGRU), a variant of cyclic neural network (RNN), can not only deal with nonlinear components well, but also deal with the problem of insufficient dependence over long distances and has a simple structure. Autoregressive integrated moving average (ARIMA) has the advantage of being sensitive to linear components. Firstly, the characteristics of the pre-processed pump station data are selected and screened through Pearson correlation coefficient and a self-attention mechanism. Then, a bi-directional gated recurrent unit (BiGRU) is used to process the nonlinear components of the data, and a dropout layer is added to avoid overfitting phenomena. We extract the linear features of the obtained error terms using the ARIMA model and use them as correction items to correct the prediction results of the BiGRU model. Finally, we obtain the prediction results of the overflow and water level. The variation characteristics of overdischarge are analyzed by the relation of flow and water level. In this paper, the actual production data of a Grade 9 pumping station of Miyun Reservoir is taken as an example to verify the validity of the model. Model performance is evaluated according to mean absolute error (MAE), mean absolute percentage error (MAPE) and linear regression correlation coefficient (R2). The experimental results show that, compared with the single ARIMAX, BiGRU model and BP neural network, the SA–BiGRU–ARIMA hybrid prediction model has a better prediction effect than other data-driven models.

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Prediction of water flows in Colorado River, Argentina

Cited by 20 publications

References 38 publications

Retraction: Jin, J, Wu, T, Zhang, Z, Fan, J, Lee, X. Application of optimization‐based estimation analysis for predicting the compressive strength of eco‐friendly modified geopolymer concrete. Structural Concrete. 2022. https://doi.org/10.1002/suco.202201051

Retraction: Jin, J, Wu, T, Zhang, Z, Fan, J, Lee, X. Application of optimization‐based estimation analysis for predicting the compressive strength of eco‐friendly modified geopolymer concrete. Structural Concrete. 2022. https://doi.org/10.1002/suco.202201051

Using two soft computing methods to predict wall and bed shear stress in smooth rectangular channels

Overflow Capacity Prediction of Pumping Station Based on Data Drive

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