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Tropical Storm Path Prediction Using Long Short-Term Memory Model, Similarity Measurement of Trajectories and Contextual Information
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Sahar Farmanifard   , Ali Asghar Alesheikh , Mohammad Sharif * , Danial Alizadeh |
| University of Hormozgan |
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Abstract: (1262 Views) |
| Tropical Cyclones are a natural and complex phenomenon that threatens the life and property of human society. The accuracy of predicting their trajectories is critical to reducing economic loss and saving human lives. When a storm occurs, context information such as wind speed and intensity, air pressure, storm direction, water surface temperature, etc. are effective in changing the direction of storm trajectory. Accordingly, considering this informations can improve the forecasting accuracy. Researchers have used various methods to predict the direction of hurricane movements to achieve the highest accuracy in forecasting. Recently, deep learning methods have shown a potential capability to process complex data efficiently and accurately. In this paper, we used the Long Short-Term Memory method to predict the future path and location of tropical cyclones in the North Atlantic Ocean by measuring the similarity of tropical cyclone trajectories and taking into account positional parameters and context information such as wind speed and storm direction. The obtained results show an improvement in the accuracy of the prediction compared to the lack of context information for the 3, 6, 9, and 12 hours time periods. The distance between the predicted trajectory path and actual trajectory path has been reduced from 1.9 to 4.5 km, taking into account the context information. |
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| Keywords: Trajectory Prediction, Similarity Analysis, Contextual Information, Long Short-Term Memory, Tropical Storms |
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Full-Text [PDF 1148 kb]
(209 Downloads)
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Type of Study: Research |
Subject:
GIS
Received: 2021/05/30 | Accepted: 2021/10/27 | ePublished ahead of print: 2023/08/28 | Published: 2023/10/10
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