Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions
Author
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-23
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
Taiwan, being located on a path in the west Pacific Ocean where typhoons often strike, is often affected by typhoons.
The accompanying strong winds and torrential rains make typhoons particularly damaging in Taiwan.
Therefore, we aimed to establish an accurate wind speed prediction model for future typhoons, allowing for better preparation to mitigate a typhoon’s toll on life and property.
For more accurate wind speed predictions during a typhoon episode, we used cutting-edge machine learning techniques to construct a wind speed prediction model.
To ensure model accuracy, we used, as variable input, simulated values from the Weather Research and Forecasting model of the numerical weather prediction system in addition to adopting deeper neural networks that can deepen neural network structures in the construction of estimation models.
Our deeper neural networks comprise multilayer perceptron (MLP), deep recurrent neural networks (DRNNs), and stacked long short-term memory (LSTM).
These three model-structure types differ by their memory capacity: MLPs are model networks with no memory capacity, whereas DRNNs and stacked LSTM are model networks with memory capacity.
A model structure with memory capacity can analyze time-series data and continue memorizing and learning along the time axis.
The study area is northeastern Taiwan.
Results showed that MLP, DRNN, and stacked LSTM prediction error rates increased with prediction time (1–6 hours).
Comparing the three models revealed that model networks with memory capacity (DRNN and stacked LSTM) were more accurate than those without memory capacity.
A further comparison of model networks with memory capacity revealed that stacked LSTM yielded slightly more accurate results than did DRNN.
Additionally, we determined that in the construction of the wind speed prediction model, the use of numerically simulated values reduced the error rate approximately by 30%.
These results indicate that the inclusion of numerically simulated values in wind speed prediction models enhanced their prediction accuracy.
American Psychological Association (APA)
Wei, Chih-Chiang. 2020. Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions. Advances in Meteorology،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1126902
Modern Language Association (MLA)
Wei, Chih-Chiang. Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions. Advances in Meteorology No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1126902
American Medical Association (AMA)
Wei, Chih-Chiang. Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions. Advances in Meteorology. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1126902
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126902