| Title | A Nonlinear Artificial Intelligence Ensemble Prediction Model for Typhoon Intensity |
| Authors | Jin, Long Yao, Cai Huang, Xiao-Yan |
| Affiliation | Guangxi Res Inst Meteorol Disasters Mitigat, Nanning 530022, Peoples R China. Peking Univ, Sch Phys, Dept Atmospher Sci, Beijing 100871, Peoples R China. |
| Keywords | TROPICAL CYCLONE MOTION GENETIC ALGORITHM NEURAL-NETWORK PACIFIC SCHEME SYSTEM |
| Issue Date | 2008 |
| Publisher | monthly weather review |
| Citation | MONTHLY WEATHER REVIEW.2008,136,(12),4541-4554. |
| Abstract | A new nonlinear artificial intelligence ensemble prediction (NAIEP) model has been developed for predicting typhoon intensity based on multiple neural networks with the same expected output and using an evolutionary genetic algorithm (GA). The model is validated with short-range forecasts of typhoon intensity in the South China Sea (SCS); results show that the NAIEP model is clearly better than the climatology and persistence (CLIPER) model for 24-h forecasts of typhoon intensity. Using identical predictors and sample cases, predictions of the genetic neural network (GNN) ensemble prediction (GNNEP) model are compared with the single-GNN prediction model, and it has been proven theoretically that the former is more accurate. Computation and analysis of the generalization capacity of GNNEP also demonstrate that the prediction of the ensemble model integrates predictions of its optimized ensemble members, so the generalization capacity of the ensemble prediction model is also enhanced. This model better addresses the "overfitting" problem that generally exists in the traditional neural network approach to practical weather prediction. |
| URI | http://hdl.handle.net/20.500.11897/397089 |
| ISSN | 0027-0644 |
| DOI | 10.1175/2008MWR2269.1 |
| Indexed | SCI(E) EI |
| Appears in Collections: | 物理学院 |
