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气象:2014,40(7):806-815
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基于局部线性嵌入的人工智能台风强度集合预报模型
(1.广西气象减灾研究所,南宁 530022;2.广西区气候中心,南宁 530022;3.华东师范大学计算机科学技术系,上海 200241)
An Artificial Intelligence Ensemble Prediction Scheme for Typhoon Intensity Using the Locally Linear Embedding
(1.Guangxi Research Institute of Meteorological Disasters Mitigation, Nanning 530022;2.Guangxi Climate Centre, Nanning 530022;3.Department of Computer Science and Technology, East China Normal University, Shanghai 200241)
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投稿时间:2013-07-29    修订日期:2013-11-05
中文摘要: 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。
Abstract: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 development 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 Locally 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 extraction. 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. According 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, respectively, 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 modeling method for the objective prediction of typhoon intensity.
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基金项目:国家自然科学基金项目(41065002)、广西自然科学基金项目(2011GXNSFE018006和2011GXNSFA018008)及国家自然科学青年基金项目(61203301)共同资助
引用文本:
黄颖,金龙,黄小燕,史旭明,金健,2014.基于局部线性嵌入的人工智能台风强度集合预报模型[J].气象,40(7):806-815.
HUANG Ying,JIN Long,HUANG Xiaoyan,SHI Xuming,JIN Jian,2014.An Artificial Intelligence Ensemble Prediction Scheme for Typhoon Intensity Using the Locally Linear Embedding[J].Meteor Mon,40(7):806-815.