CAJ | 학술논문

提利用局部线性嵌入算法通过学习挖掘高维数据集的内在几何结构，高效地实现维数约简和特征提取的能力，论文以2001-2012年共12年6-9月西北太平洋海域内生成的台风样本为基础，将气候持续因子作为台风强度的基本预报因子，采用局部线性嵌入的特征提取与逐步回归计算相结合的预报因子信息数据挖掘技术，以进化计算的粒子群算法，生成期望输出相同的多个神经网络个体，建立了一种新的非线性人工智能集合预报模型，进行了分月台风强度预报模型的建模研究。在建模样本、独立预报样本相同的情况下，分别采用人工智能集合预报方法和气候持续法进行预报试验。试验对比结果表明，前者较后者在6、7、8和9月24 h台风强度预报中，平均绝对误差分别下降了23．34％、24．46％、19．41％和27．45％，4个月的平均绝对误差下降了23．10％；48 h台风强度预报中，6-9月平均绝对误差分别下降了44．82％、16．73％、0．89％和49．26％，4个月的平均绝对误差下降了25．54％。进一步研究发现，在变动局部线性嵌入算法k近邻个数的情况下，建立的台风强度集合预报模型，其预报结果稳定可靠，相对于气候持续法均为正的预报技巧水平，为台风强度客观预报提供了新的预报工具和预报建模方法。
제이용국부선성감입산법통과학습알굴고유수거집적내재궤하결구，고효지실현유수약간화특정제취적능력，논문이2001-2012년공12년6-9월서북태평양해역내생성적태풍양본위기출，장기후지속인자작위태풍강도적기본예보인자，채용국부선성감입적특정제취여축보회귀계산상결합적예보인자신식수거알굴기술，이진화계산적입자군산법，생성기망수출상동적다개신경망락개체，건립료일충신적비선성인공지능집합예보모형，진행료분월태풍강도예보모형적건모연구。재건모양본、독립예보양본상동적정황하，분별채용인공지능집합예보방법화기후지속법진행예보시험。시험대비결과표명，전자교후자재6、7、8화9월24 h태풍강도예보중，평균절대오차분별하강료23．34％、24．46％、19．41％화27．45％，4개월적평균절대오차하강료23．10％；48 h태풍강도예보중，6-9월평균절대오차분별하강료44．82％、16．73％、0．89％화49．26％，4개월적평균절대오차하강료25．54％。진일보연구발현，재변동국부선성감입산법k근린개수적정황하，건립적태풍강도집합예보모형，기예보결과은정가고，상대우기후지속법균위정적예보기교수평，위태풍강도객관예보제공료신적예보공구화예보건모방법。
A Northwest Pacific typhoon intensity prediction scheme has been developed based on multiple neural networks with the same expected output and an evolutionary Particle Swarm Optimization (PSO) algorithm.Typhoon samples during June-September spanning 2001-2012 are used for model develop-ment and Climatology and Persistence (CLIPER)factors are used as potential predictors.The new model input is constructed from potential predictors by employing both a stepwise regression method and a Local-ly Linear Embedding (LLE)algorithm.The LLE algorithm is able to learn and identify the underlying structure of a high-dimensional vector space,and then perform dimensionality reduction and feature extrac-tion.In this scheme,the new developed model,which is termed the PNN-LLE model,is used for monthly typhoon intensity prediction at 24- and 48-h lead time.Using identical modeling samples and independent samples,predictions of the PNN-LLE model are compared with the widely used CLIPER method.Accord-ing to the statistics,the PNN-LLE model shows reductions of the mean absolute errors of 23.34%, 24.46%,19.41% and 27.45% relative to the CLIPER method for June-September 24-h forecasts,re-spectively,being 23.10% for the 4 months averagely.From June-September the mean absolute errors of the PNN-LLE model are 44.82%,16.73%,0.89%and 49.26%more skillful than homogeneous CLIPER intensity forecasts for 48-h forecast,respectively,being 25 .54% for the 4 months averagely.By adopting different numbers of nearest neighbors in the LLE algorithm,sensitivity experiments further show that the prediction results of the ensemble model are stable and reliable,and the forecast skill level of the ensemble model is better than that of the CLIPER method,potentially providing operational forecast tool and model-ing method for the objective prediction of typhoon intensity.