Name
Title | ||
|---|---|---|
An Artificial Neural Network Model for Predicting Typhoon Intensity and Its Application. |
Abstract | ||
|---|---|---|
Considering that the typhoon intensity's statistical predictors have the characteristics of inaccuracy, incompleteness and uncertainty, and the optional factors are factors are usually lots in a practical application, but the predictive ability will decline if using too many factors in a model, and may also lost the important information by choosing the inappropriate factorsQuery. Latitude and longitude of storm center, minimum central pressure, maximum wind speed near the storm center were chosen to be predictors, and a neural network model for predicting typhoon intensity was established by using every 6 h of current and former 18 h of these information directly. In this study, 61-year data set from 1949 to 2009 was used to train the networks, and 5-year data set from 2010 to 2014 was used to test the trained network. Compared with other typhoon predicting models, and results showed that the model has obtained a good predicting accuracy. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-981-10-6364-0_75 | ADVANCED COMPUTATIONAL METHODS IN ENERGY, POWER, ELECTRIC VEHICLES, AND THEIR INTEGRATION, LSMS 2017, PT 3 |
Keywords | DocType | Volume |
Typhoon intensity,Artificial neural network,Predicting model | Conference | 763 |
ISSN | Citations | PageRank |
1865-0929 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruyun Wang | 1 | 0 | 0.34 |
| Wang Tian | 2 | 17 | 15.16 |
| Xiaoyu Zhang | 3 | 112 | 23.80 |
| Qing Fang | 4 | 2 | 2.19 |
| Chumin Wu | 5 | 0 | 0.34 |
| Bin Zhang | 6 | 60 | 40.23 |