A long short‐term memory‐based model for greenhouse climate prediction

2021 ◽  
Author(s):  
Yuwen Liu ◽  
Dejuan Li ◽  
Shaohua Wan ◽  
Fan Wang ◽  
Wanchun Dou ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Climate Prediction ◽  
Short Term ◽  
Term Memory ◽  
Greenhouse Climate ◽  
Long Short Term Memory

Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Financial News ◽  
Long Short Term Memory

Unit Selection with Hierarchical Cascaded Long Short Term Memory Bidirectional Recurrent Neural Nets

2017 ◽  
Author(s):  
Vincent Pollet ◽  
Enrico Zovato ◽  
Sufian Irhimeh ◽  
Pier Batzu
Keyword(s):  
Short Term Memory ◽  
Neural Nets ◽  
Short Term ◽  
Unit Selection ◽  
Term Memory ◽  
Long Short Term Memory

Articulatory-to-speech Conversion Using Bi-directional Long Short-term Memory

2018 ◽  
Author(s):  
Fumiaki Taguchi ◽  
Tokihiko Kaburagi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism

2021 ◽  
Vol 11 (14) ◽  
pp. 6625
Author(s):  
Yan Su ◽  
Kailiang Weng ◽  
Chuan Lin ◽  
Zeqin Chen
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Impact Factors ◽  
Nonlinear Prediction ◽  
Time Dimension ◽  
Short Term ◽  
Deformation Prediction ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Dam Deformation

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.

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