CAJ | 학술논문

为了解主成分分析在线性模型与非线性模型预报中的应用效果，在2001—2011年热带气旋历史观测资料基础上，采用主成分分析方法，结合线性回归模型和神经网络模型，开展西北太平洋热带气旋的强度预报技术研究试验．根据提取的主要影响因子构造线性回归模型与BP神经网络的输入样本进行不同样本的台风强度预测．计算结果表明，主成分分析通过降低线性回归模型和BP神经网络模型的维数，减少自变量之间的复共线性，减小模型的预报平均绝对误差．
위료해주성분분석재선성모형여비선성모형예보중적응용효과，재2001—2011년열대기선역사관측자료기출상，채용주성분분석방법，결합선성회귀모형화신경망락모형，개전서북태평양열대기선적강도예보기술연구시험．근거제취적주요영향인자구조선성회귀모형여BP신경망락적수입양본진행불동양본적태풍강도예측．계산결과표명，주성분분석통과강저선성회귀모형화BP신경망락모형적유수，감소자변량지간적복공선성，감소모형적예보평균절대오차．
In order to evaluate the potential forecast efficiency of principal components analysis (PCA) in linear model and nonlinear model, based on 2001-2011 historical tropical cyclone observation data, the PCA efficiency are investigated through multiple linear regression model and neural network model focusing on the northwestern Pacific Ocean tropical cyclone intensity prediction technology. According to these main factors, the input samples of linear regress model and BP neutral network are definite, and the models could be trained to predict tropical cyclone intensity. Result shows that PCA reduces the models dimension of line- ar regression and BP neural network, and weaken multi-collinearity among the independent variables, and the method based on PCA lessens average absolute error (MAE) of tropical cyclone intensity.