Optimized Scenario for Estimating Suspended Sediment Yield Using an Artificial Neural Network Coupled with a Genetic Algorithm

Yadav, Arvind; Hasan, Mohammad Kamrul; Joshi, Deeksha; Kumar, Vinod; Aman, Azana Hafizah Mohd; Alhumyani, Hesham; Alzaidi, Mohammed S.; Mishra, Haripriya

doi:10.3390/w14182815

Cited by 7 publications

(4 citation statements)

References 91 publications

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“…Figure 12 shows the trend plots of the predicted and tested values of the absorption coefficients obtained under the four optimization algorithms. If the distance between each point and y = x (red line in the figure) is short and uniformly distributed around it, it indicates a better prediction [ 42 ]. In contrast, the results in the figure show that only the EO-GRNN has excellent accuracy, while the other three are less uniformly distributed and have a more considerable distance from y = x.…”

Section: Resultsmentioning

confidence: 99%

Prediction of the Sound Absorption Coefficient of Three-Layer Aluminum Foam by Hybrid Neural Network Optimization Algorithm

Guo

Lisi

et al. 2022

Materials

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The combination of multilayer aluminum foam can have high sound absorption coefficients (SAC) at low and medium frequencies, and predicting its absorption coefficient can help the optimal structural design. In this study, a hybrid EO-GRNN model was proposed for predicting the sound absorption coefficient of the three-layer composite structure of the aluminum foam. The generalized regression neural network (GRNN) model was used to predict the sound absorption coefficient of three-layer composite structural aluminum foam due to its outstanding nonlinear problem-handling capability. An equilibrium optimization (EO) algorithm was used to determine the parameters in the neuronal network. The prediction results show that this method has good accuracy and high precision. The calculation result shows that this proposed hybrid model outperforms the single GRNN model, the GRNN model optimized by PSO (PSO-GRNN), and the GRNN model optimized by FOA(FOA-GRNN). The prediction results are expressed in terms of root mean square error (RMSE), absolute error, and relative error, and this method performs well with an average RMSE of only 0.011.

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Section: Resultsmentioning

confidence: 99%

Prediction of the Sound Absorption Coefficient of Three-Layer Aluminum Foam by Hybrid Neural Network Optimization Algorithm

Guo

Lisi

et al. 2022

Materials

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“…Over the past decade, numerous machine learning and soft computing methods have been used in civil engineering, water resources science, hydrology, and hydraulics to model complex phenomena (Seyedian and Rouhani, 2015;Yadav et al, 2022). Out of the many available machine learning techniques, we chose Least Square Support Vector Regression (LSSVR), Quantile Regression Forest (QRF), and Gaussian Process Regression (GPR) in this study due to their computational efficiency, ease of training, and ability to provide Prediction Intervals (PI).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis of discharge coefficient predicted for rectangular side weir using machine learning methods

Seyedian,

Kisi

2024

Journal of Hydrology and Hydromechanics

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The present study used three machine learning models, including Least Square Support Vector Regression (LSSVR) and two non-parametric models, namely, Quantile Regression Forest (QRF) and Gaussian Process Regression (GPR), to quantify uncertainty and precisely predict the side weir discharge coefficient (Cd) in rectangular channels. So, 15 input structures were examined to develop the models. The results revealed that the machine learning models used in the study offered better accuracy compared to the classical equations. While the LSSVR and QRF models provided a good prediction performance, the GPR slightly outperformed them. The best input structure that was developed included all four dimensionless parameters. Sensitivity analysis was conducted to identify the effective parameters. To evaluate the uncertainty in the predictions, the LSSVR, QRF, and GPR were used to generate prediction intervals (PI), which quantify the uncertainty coupled with point prediction. Among the implemented models, the GPR and LSSVR models provided more reliable results based on PI width and the percentage of observed data covered by PI. According to point prediction and uncertainty analysis, it was concluded that the GPR model had a lower uncertainty and could be successfully used to predict Cd.

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“…ANNs have been shown to perform accurately in various fields of water resources. Streamflow forecasting (Chitsaz et al 2016;Yaseen et al 2018;Araghinejad et al 2018;Thakur et al, 2020, Ghobadi & Kang;Mo et al, 2023;Tyson et al, 2023), predicting groundwater levels (Khalil et al 2015;Naderianfar et al 2017;Sahoo et al, 2017 ;Sattari et al, 2018;Dadhich et al, 2021;Navale & Mhaske, 2023), drought forecasting (Hosseini-Moghari and Araghinejad 2015;Mokhtarzad et al 2017;Khan et al, 2020;Alawsi et al, 2022), flood forecasting (Latt and Wittenberg 2014;Alexander and Thampi 2018;Dtissibe et al, 2020;Wang et al, 2022), sediment estimation (Banihabib and Emami 2017;Zounemat-Kermani et al 2018;Banadkooki et al, 2020;Yadav et al, 2022) and evaporation modeling (Antonopoulos 2016;Nourani et al, 2020;Arya Azar et al, 2023) are the most common uses of ANN in hydrology. In many studies, R-R modeling is conducted by employing an ANN.…”

Section: Introductionmentioning

confidence: 99%

Application of Gridded Precipitation Datasets for Simulating Discharge along with Hybrid Machine Learning Methods: An Operational Approach for Poorly Gauged Basins

Morovati

2023

Preprint

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In many regions, there is no long-term discharge data which do not include any gaps. In this work, we have tried to overcome these limitations with the use of gridded precipitation datasets and data-driven modeling. To this end, the Multilayer Perceptron Neural Network (MLPNN), as a Rainfall-Runoff (R-R) model was taken into account to simulate the discharge of the Karkheh basin in Iran. Precipitation data was extracted from Asian Precipitation-Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE), Global Precipitation Climatology Center (GPCC) and Climatic Research Unit (CRU) datasets. MLPNN training was implemented using the Levenberg-Marquardt (LM) algorithm and the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) were used to pre-process input data for the MLPNN as well. Two scenarios were considered for R-R modeling. In Scenario1 (S1), the model was calibrated via in situ data and the dataset data was used in the testing phase. In Scenario 2 (S2), the model was calibrated and examined separately based on each dataset. The results showed that in S1, APHRODITE outperformed the other two datasets. All dataset functions were improved in S2. To sum up, the best performance of APHRODITE, GPCC, and CRU is related to hybrid applications of S2-PCA-NSGA-II, S2-SVD-NSGA-II, and S2-SVD-NSGA-II, respectively. Our results indicate that, the main error found in the gridded precipitation dataset is related to bias error which will be disappeared automatically when the model is calibrated using gridded precipitation datasets, suggesting that the bias correction or re-calibration of existing models are required. The results illustrate high potential of gridded precipitation dataset and data-driven models in runoff simulation or filling the gaps existed in observed data.

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Optimized Scenario for Estimating Suspended Sediment Yield Using an Artificial Neural Network Coupled with a Genetic Algorithm

Cited by 7 publications

References 91 publications

Prediction of the Sound Absorption Coefficient of Three-Layer Aluminum Foam by Hybrid Neural Network Optimization Algorithm

Prediction of the Sound Absorption Coefficient of Three-Layer Aluminum Foam by Hybrid Neural Network Optimization Algorithm

Uncertainty analysis of discharge coefficient predicted for rectangular side weir using machine learning methods

Application of Gridded Precipitation Datasets for Simulating Discharge along with Hybrid Machine Learning Methods: An Operational Approach for Poorly Gauged Basins

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