| Autor: |
Ma, Enlin, Janiszewski, Mateusz, Torkan, Masoud |
| Přispěvatelé: |
Anagnostou, Georgios, Benardos, Andreas, Marinos, Vassilis P., Chang'an University, Department of Civil Engineering, Mineral Based Materials and Mechanics, Aalto-yliopisto, Aalto University |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Popis: |
Prediction is a vague concept that is why we need to conceptualize it specifically for underground deformation time-series data. For this impending issue, this paper employs an advanced deep learning model Bi-LSTM-AM to address it. The results show its applicability for practical engineering. The proposed model is compared with other basic deep learning models including long short-term memory (LSTM), Bi-LSTM, gated recurrent units (GRU), and temporal convolutional networks (TCN). These models cover the most common three forms of deep learning for time-series prediction: recurrent neural networks (RNN) and convolutional neural networks (CNN). This research is supposed to benefit the underground deformation time-series prediction. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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