Chromium distribution forecasting using multilayer perceptron neural network and multilayer perceptron residual kriging

Tarasov, Dmitry; Buevich, Alexander; Shichkin, Andrey; Subbotina, Irina; Tyagunov, Andrey; Baglaeva, Elena

doi:10.1063/1.5044048

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…In the atmospheric field, NNRK is used to predict spatial precipitation [34]. In the geological field, NNRK is used to predict the distribution of minerals or pollutants in soil [35][36][37][38][39]. In other fields, NNRK also has some applications [40,41].…”

Section: Introductionmentioning

confidence: 99%

Flight Load Calculation Using Neural Network Residual Kriging

2023

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Flight load calculation, an important step in aircraft design and optimization, typically involves millions of computations and requires significant computing resources and time. Improving the efficiency of flight load calculations while maintaining accuracy is therefore of great significance for shortening research and development cycles. This study investigated and compared multiple algorithms, including the neural network model, the Kriging surrogate model, and the neural network residual Kriging (NNRK) model, for flight load analysis. The accuracies of all models were confirmed through evaluation, with NNRK being the most efficient, making it highly suitable for flight load analysis. The flight load data of a civil aircraft, including the total weight, the center of gravity, the pitch moment of inertia, the altitude, the Mach number, the airspeed, the velocity pressure, the pitch rate, the load factor, and the angle of attack as input parameters, were used as sample data to establish models, for predicting wing loads under different flight conditions. The accuracies of all regressions were confirmed through evaluation, with NNRK being the most efficient. The flight load calculation shows that NNRK can significantly improve analysis efficiency and provide new insights into efficient and comprehensive flight load analysis.

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Section: Introductionmentioning

confidence: 99%

Flight Load Calculation Using Neural Network Residual Kriging

2023

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“…Recently there have been a new trend in DSM that foster the combination of geostatitics and MLA in mapping and prediction. Several soil scientist and authors such as Sergeev et al, 23 ; Subbotina et al, 24 ; Tarasov et al, 25 and Tarasov et al, 26 have harness accurate qualities in geostatistics and machine learning to generate hybrid models that increase the efficiency and quality of the prediction as well as mapping. Some of these hybrization or combined algrorithmic models are artificial nueral network-kriging (ANN-RK), multi-layer perceptron residual kriging (MLP-RK), generalized regression neural network residual kriging (GR-NNRK) 25 and artificial nueral network-kriging-multilayer perceptron (ANN-K-MLP) .…”

Section: Introductionmentioning

confidence: 99%

“…Some of these hybrization or combined algrorithmic models are artificial nueral network-kriging (ANN-RK), multi-layer perceptron residual kriging (MLP-RK), generalized regression neural network residual kriging (GR-NNRK) 25 and artificial nueral network-kriging-multilayer perceptron (ANN-K-MLP) . 26 According to Sergeev et al, 23 the act of combining various modelling techniques has the potential to eliminate flaws and increase the efficiency of the hybrid model produced over the single models from which it was developed. Against this backdrop, this new paper deem it necessary to apply a combined algorithm from geostatistic and MLA to develop the best hybridized model to predict the enrichment of Ni in the urban and peri-urban area.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Nickel Concentrations in Urban and Peri-Urban Soils: Application of a Hybridized Empirical Bayesian Kriging and Support Vector Machine Regression Approach

Kebonye

John

2021

Preprint

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Soil pollution is a big issue caused by anthropogenic activities. The spatial distribution of potentially toxic elements (PTEs) varies in most urban and peri-urban areas. As a result, spatially predicting the PTEs content in such soil is difficult. A total number of 115 samples were obtained from Frydek Mistek in the Czech Republic. Calcium(Ca), magnesium(Mg), potassium(K), and nickel (Ni) concentrations were determined using Inductively Coupled Plasma Optical Emission Spectroscopy. The correlation matrix between the response variable and the predictors revealed a satisfactory correlation between the elements. The prediction results indicated that support vector machine regression (SVMR) performed well although its estimated root mean square error (RMSE) (235.974) and mean absolute error (MAE) (166.946) were higher when compared with the other methods applied. Conversely, the hybridized model of empirical bayesian kriging -multiple linear regression (EBK-MLR) performed poorly as indicated by the measured coefficient of determination value below 0.1. The empirical bayesian kriging-support vector machine regression (EBK-SVMR) model was the best model, with low RMSE (95.479) and MAE (77.368) values and a high coefficient of determination (R2 = 0.637). EBK-SVMR modeling technique was visualized using self-organizing map. The clustered neurons of the hybridized model CakMg -EBK-SVMR component plane showed a diverse color pattern predicting the concentration of Ni in the urban and peri urban soil. The results proved that combining EBK and SVMR is an effective technique for predicting Ni concentrations in urban and peri-urban soil.

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