Name
Abstract | ||
|---|---|---|
Huge losses of life and economy are brought by Tropical Cyclones(TCs). Accurate TC intensity prediction is crucial for disaster prevention and emergency decision-making, but the chaotic nature of TC makes the intensity prediction a challenging task. Recently, TC intensity prediction has been regarded as a spatio-temporal prediction problem by a surging number of researchers, and deep learning methods have been used to model both spatial and temporal characteristics of which. However, existing studies on TC intensity prediction based on deep learning methods are still inadequate in spatio-temporal feature modeling and prediction granularity. In this study, a neural network framework specifically for TC intensity prediction named TC-Pred is proposed. To be Specific, a novel feature extraction and aggregation approach is designed considering the characteristics of multi-source environmental variables, and the sequence-to-sequence architecture is innovatively introduced to make fine-grained predictions. Moreover, a module inspired by the convolutional transformer is developed that aims to alleviate the long-term dependency decay problem and improve model performance. Extensive experiments are performed on the environmental variables dataset to verify the effectiveness and practicability of the proposed framework. The experimental results demonstrate that most of the models based on TC-Pred have achieved better performance, and the TC-Pred(ConvGRU) model obtains a significant improvement by 18.31%, 7.01%, 14.17%, and 11.95% improvements compared with baselines at 6h, 12h, 18h, and 24h intervals, respectively. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.knosys.2022.108195 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Tropical cyclone intensity,Fine-grained prediction,Sequence-to-sequence,Neural network framework | Journal | 241 |
ISSN | Citations | PageRank |
0950-7051 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhe Zhang | 1 | 6 | 9.60 |
| Xuying Yang | 2 | 0 | 0.68 |
| Lingfei Shi | 3 | 0 | 0.34 |
| Bingbing Wang | 4 | 0 | 0.34 |
| Zhenhong Du | 5 | 31 | 16.98 |
| Feng Zhang | 6 | 3 | 4.76 |
| Liu Renyi | 7 | 15 | 13.13 |