A Nonlinear Artificial Intelligence Ensemble Prediction Model for Typhoon Intensity
A Nonlinear Artificial Intelligence Ensemble Prediction Model for Typhoon Intensity
复制标题
台风强度的非线性人工智能集成预测模型
DOI：
10.1175/2008mwr2269.1
复制
发表时间：
2008-12-01
影响因子：
3.2
通讯作者：
Huang, Xiao-Yan
中科院分区：
文献类型：
--
作者：
Jin, Long;Yao, Cai;Huang, Xiao-Yan
基于具有相同预期输出的多个神经网络，采用进化遗传算法（GA），建立了一种新的非线性人工智能集成预测（NAIEP）台风强度模型。用南海台风强度的短期预报对模型进行了验证；结果表明，NAIEP模式在24 h台风强度预报上明显优于CLIPER模式。利用相同的预测器和样本案例，将遗传神经网络（GNN）集合预测（GNNEP）模型的预测结果与单一GNN预测模型的预测结果进行了比较，从理论上证明了前者的预测精度更高。对GNNEP泛化能力的计算和分析也表明，集成模型的预测集成了其优化的集成成员的预测，因此集成预测模型的泛化能力也得到了增强。该模型较好地解决了传统神经网络方法在实际天气预报中普遍存在的“过拟合”问题。
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.