TITLE

A Nonlinear Artificial Intelligence Ensemble Prediction Model for Typhoon Intensity

AUTHOR(S)
Long Jin; Cai Yao; Xiao-Yan Huang
PUB. DATE
December 2008
SOURCE
Monthly Weather Review;Dec2008, Vol. 136 Issue 12, p4541
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
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.
ACCESSION #
36092266

 

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