Multistep prediction for earthworks unloading duration: a fuzzy Att-Seq2Seq network with optimal partitioning and multi-time granularity modeling

Zhang, Yunuo; Wang, Xiaoling; Yu, Jia; Zeng, Tuocheng; Wang, Jiajun

doi:10.1007/s00521-023-08883-5

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2024

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Cited by 3 publications

References 59 publications

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Adaboosting graph attention recurrent network: A deep learning framework for traffic speed forecasting in dynamic transportation networks with spatial-temporal dependencies

Zhang,

Wang,

et al. 2024

Engineering Applications of Artificial Intelligence

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Adaboosting graph attention recurrent network: A deep learning framework for traffic speed forecasting in dynamic transportation networks with spatial-temporal dependencies

Zhang,

Wang,

et al. 2024

Engineering Applications of Artificial Intelligence

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Exploiting Fuzzy Logic for Time Series Classification in Networks

Bishnoi,

Chethan,

Bhagat

2024

2024 3rd International Conference for Innovation in Technology (INOCON)

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Prediction of Ship-Unloading Time Using Neural Networks

Gao,

Li,

Wang

et al. 2024

Applied Sciences

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The prediction of unloading times is crucial for reducing demurrage costs and ensuring the smooth scheduling of downstream processes in a steel plant. The duration of unloading a cargo ship is primarily determined by the unloading schedule established at the raw materials terminal and the storage operation schedule implemented in the stockyard. This study aims to provide an accurate forecast of unloading times for incoming ships at the raw materials terminal of a steel plant. We propose three neural network-based methods: the Backpropagation Neural Network (BP), the Random Vector Functional Link (RVFL), and the Stochastic Configurations Network (SCN) for this prediction. This issue has not been previously researched using similar methods, particularly in the context of large-scale steel plants. The performance of these three methods is evaluated based on several indices: the Root Mean Square Error (RMSE), the quality of the best solution, convergence, and stability, which are employed for predicting unloading times. The prediction accuracies achieved by the BP, RVFL, and SCN were 76%, 85%, and 87%, respectively. These results demonstrate the effectiveness and potential applications of the proposed methods.

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Multistep prediction for earthworks unloading duration: a fuzzy Att-Seq2Seq network with optimal partitioning and multi-time granularity modeling

Cited by 3 publications

References 59 publications

Adaboosting graph attention recurrent network: A deep learning framework for traffic speed forecasting in dynamic transportation networks with spatial-temporal dependencies

Adaboosting graph attention recurrent network: A deep learning framework for traffic speed forecasting in dynamic transportation networks with spatial-temporal dependencies

Exploiting Fuzzy Logic for Time Series Classification in Networks

Prediction of Ship-Unloading Time Using Neural Networks

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