Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions
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...
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Wiley
2020-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2020/5462040 |
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| author | Chih-Chiang Wei |
| author_facet | Chih-Chiang Wei |
| author_sort | Chih-Chiang Wei |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7545570a9f5f42e392e08b23110864cb |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
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| series | Advances in Meteorology |
| spelling | doaj-art-7545570a9f5f42e392e08b23110864cb2025-02-03T01:27:55ZengWileyAdvances in Meteorology1687-93091687-93172020-01-01202010.1155/2020/54620405462040Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity PredictionsChih-Chiang Wei0Department of Marine Environmental Informatics and Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung City, TaiwanTaiwan, 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.http://dx.doi.org/10.1155/2020/5462040 |
| spellingShingle | Chih-Chiang Wei Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions Advances in Meteorology |
| title | Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions |
| title_full | Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions |
| title_fullStr | Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions |
| title_full_unstemmed | Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions |
| title_short | Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions |
| title_sort | development of stacked long short term memory neural networks with numerical solutions for wind velocity predictions |
| url | http://dx.doi.org/10.1155/2020/5462040 |
| work_keys_str_mv | AT chihchiangwei developmentofstackedlongshorttermmemoryneuralnetworkswithnumericalsolutionsforwindvelocitypredictions |