Name
Abstract | ||
|---|---|---|
This paper applied data mining methods to study the time-series hurricane data of 28 years (1982-2009) in Atlantic region to improve the accuracy of hurricane intensity prediction. According to the contrast test of base classifiers, IBk classifier and the LMT classifier were selected and integrated by using the Bagging method. The optimized Bagging base classifier sequence was trained using the optimal training data set after culling abnormal data. Then the hurricane intensity prediction model: the Bagging-IBk&LMT model was formed. The testing result showed that the prediction model was better than single classification model. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/Multi-Temp.2019.8866954 | 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) |
Keywords | Field | DocType |
hurricane intensity prediction,data mining,time series data | Training set,Data modeling,Time series data mining,Pattern recognition,Computer science,Artificial intelligence,Classifier (linguistics),Statistical classification,Tropical cyclone | Conference |
ISBN | Citations | PageRank |
978-1-7281-4616-4 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuhan Yang | 1 | 0 | 0.34 |
| Qingxiang Meng | 2 | 5 | 1.10 |