A Tailings Dam Long-Term Deformation Prediction Method Based on Empirical Mode Decomposition and LSTM Model Combined with Attention Mechanism

A Tailings Dam Long-Term Deformation Prediction Method Based on Empirical Mode Decomposition and LSTM Model Combined with Attention Mechanism

Tailings dams are constructed as storage dams for ore waste, serving as industrial waste piles and for drainage. The dam is negatively affected by rainfall, infiltration lines and its own gravity, which can cause its instability to gradually increase, leading to dam deformation. To predict the irreg...

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Bibliographic Details
Main Authors: Cao, G. (Author), Gao, Y. (Author), Li, W. (Author), Lu, S. (Author), Nie, W. (Author), Wang, R. (Author), Wang, Z. (Author), Zhang, Z. (Author), Zhu, Y. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
attention mechanism
Attention mechanisms
Dam deformation
Deformation
Digital storage
Embankment dams
empirical mode decom-position
Empirical mode decom-position
Empirical Mode Decomposition
Forecasting
lag order
Lag order
Long short-term memory
Long short-term memory network
LSTM network
Memory modeling
Memory network
Signal processing
Tailing dam
Tailing dam deformation
Tailings
tailings dam deformation
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