Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds
Wind-sensitive structures usually suffer from violent vibrations or severe damages under the action of tropical storms. It is of great significance to forecast tropical-storm winds in advance for the sake of reducing or avoiding consequent losses. The model used for forecasting becomes a primary con...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/20/9441 |
| _version_ | 1797515465385312256 |
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| author | Tianyou Tao Peng Shi Hao Wang Lin Yuan Sheng Wang |
| author_facet | Tianyou Tao Peng Shi Hao Wang Lin Yuan Sheng Wang |
| author_sort | Tianyou Tao |
| collection | DOAJ |
| description | Wind-sensitive structures usually suffer from violent vibrations or severe damages under the action of tropical storms. It is of great significance to forecast tropical-storm winds in advance for the sake of reducing or avoiding consequent losses. The model used for forecasting becomes a primary concern in engineering applications. This paper presents a performance evaluation of linear and nonlinear models for the short-term forecasting of tropical storms. Five extensively employed models are adopted to forecast wind speeds using measured samples from the tropical storm Rumbia, which facilitates a comparison of the predicting performances of different models. The analytical results indicate that the autoregressive integrated moving average (ARIMA) model outperforms the other models in the one-step ahead prediction and presents the least forecasting errors in both the mean and maximum wind speeds. However, the support vector regression (SVR) model has the worst performance on the selected dataset. When it comes to the multi-step ahead forecasting, the prediction error of each model increases as the number of steps expands. Although each model shows an insufficient ability to capture the variation of future wind speed, the ARIMA model still appears to have the least forecasting errors. Hence, the ARIMA model can offer effective short-term forecasting of tropical-storm winds in both one-step and multi-step scenarios. |
| first_indexed | 2024-03-10T06:45:52Z |
| format | Article |
| id | doaj.art-f99789b87bf74a00a10620613a0295b2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:45:52Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f99789b87bf74a00a10620613a0295b22023-11-22T17:18:37ZengMDPI AGApplied Sciences2076-34172021-10-011120944110.3390/app11209441Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm WindsTianyou Tao0Peng Shi1Hao Wang2Lin Yuan3Sheng Wang4Key Laboratory of C&PC Structures of Ministry of Education, Southeast University, Nanjing 211189, ChinaSchool of Civil Engineering, Southeast University, Nanjing 211189, ChinaKey Laboratory of C&PC Structures of Ministry of Education, Southeast University, Nanjing 211189, ChinaSchool of Civil Engineering, Southeast University, Nanjing 211189, ChinaSchool of Civil Engineering, Southeast University, Nanjing 211189, ChinaWind-sensitive structures usually suffer from violent vibrations or severe damages under the action of tropical storms. It is of great significance to forecast tropical-storm winds in advance for the sake of reducing or avoiding consequent losses. The model used for forecasting becomes a primary concern in engineering applications. This paper presents a performance evaluation of linear and nonlinear models for the short-term forecasting of tropical storms. Five extensively employed models are adopted to forecast wind speeds using measured samples from the tropical storm Rumbia, which facilitates a comparison of the predicting performances of different models. The analytical results indicate that the autoregressive integrated moving average (ARIMA) model outperforms the other models in the one-step ahead prediction and presents the least forecasting errors in both the mean and maximum wind speeds. However, the support vector regression (SVR) model has the worst performance on the selected dataset. When it comes to the multi-step ahead forecasting, the prediction error of each model increases as the number of steps expands. Although each model shows an insufficient ability to capture the variation of future wind speed, the ARIMA model still appears to have the least forecasting errors. Hence, the ARIMA model can offer effective short-term forecasting of tropical-storm winds in both one-step and multi-step scenarios.https://www.mdpi.com/2076-3417/11/20/9441tropical-storm windsshort-term forecastingstatistical methodlinear modelnon-linear modelperformance of prediction |
| spellingShingle | Tianyou Tao Peng Shi Hao Wang Lin Yuan Sheng Wang Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds Applied Sciences tropical-storm winds short-term forecasting statistical method linear model non-linear model performance of prediction |
| title | Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds |
| title_full | Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds |
| title_fullStr | Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds |
| title_full_unstemmed | Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds |
| title_short | Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds |
| title_sort | performance evaluation of linear and nonlinear models for short term forecasting of tropical storm winds |
| topic | tropical-storm winds short-term forecasting statistical method linear model non-linear model performance of prediction |
| url | https://www.mdpi.com/2076-3417/11/20/9441 |
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