Research on Prediction of Port Cargo Throughput based on PCA-BP Neural Network Combination Model

Du, Baochai

doi:10.1109/isctt51595.2020.00098

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…They all use Spearman's method to identify the correlation degree between the input variables and the target variable and to optimize the screening parameters according to the strength of the correlation degree. By using principal component analysis (PCA) to eliminate correlations between input variables, the dimensionality of the input variables in research on the seasonal predictions of PM2 [23], phosphorus content at the endpoint of the converter [24], and the throughput of cargo at ports [25] was reduced. It can be seen from these studies that the Spearman and PCA have shown good applicability in practice.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Concrete Using the Spearman and PCA-Based BP Neural Network

Wang

Zhao

Zhang³

et al. 2022

Wireless Communications and Mobile Computing

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Using real-time production data of concrete to predict its 28-day compressive strength has great significance for improving the engineering structure quality and overcoming the shortage of the traditional tests long period of concrete compressive strength. The current research has the shortcomings such as insufficient prediction accuracy, inadequate matching between data characteristics and model characteristics, and redundant input parameter information. This paper proposes a BP neural network prediction model optimized by Spearman and PCA. The model first uses Spearman method to reduce the number of the input variables by eliminating variables that have a low correlation with the compressive strength and then uses PCA to eliminate the correlation between material-related variables. Following this, the new uncorrelated input variables optimized by Spearman and PCA are input to the BP neural network-based model to predict the compressive strength of concrete. The results showed that it yielded the mean absolute percentage error (MAPE) of 2.78% and the root mean-squared error (RMSE) of 1.66 MPa, far lower than the error of 4.82% and 2.92 MPa obtained by the nonoptimized BP neural network, respectively. The proposed model fully exploits real-time monitoring data from the concrete mixing station, and its results are close to those of traditional tests. It has great practical significance to guide the concrete production and construction, shorten the production cycle, and reduce the project cost.

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Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Concrete Using the Spearman and PCA-Based BP Neural Network

Wang

Zhao

Zhang³

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

Port container throughput prediction method based on SSA-SVM

Wu¹,

Ma²,

Gao³

2023

HBEM

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To improve the prediction accuracy of the port cargo throughput and the applicability of the prediction model, and then provide data support for the port construction to meet the needs of port decision-making, take the monthly cargo throughput data of Shanghai Port from January 2009 to December 2022 as an example, use Pearson correlation analysis to screen 12 import and export impact factors. This paper improves the traditional SVM model, uses SSA (Sparrow Search Algorithm) to optimize the parameters c and g in SVM, and uses the model to predict. Compared with the model that uses the grid search algorithm to optimize the parameters of SVM, the model has a significant improvement in fitting and robustness, its predicted value is closer to the actual value, the prediction performance is better, and it can better reflect the actual state of the port.

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Research on Prediction of Port Cargo Throughput based on PCA-BP Neural Network Combination Model

Cited by 2 publications

References 3 publications

Prediction of Compressive Strength of Concrete Using the Spearman and PCA-Based BP Neural Network

Prediction of Compressive Strength of Concrete Using the Spearman and PCA-Based BP Neural Network

Port container throughput prediction method based on SSA-SVM

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