Abstract This paper aims to improve the prediction accuracy of Tropical Cyclone Tracks (TCTs) over the South China Sea (SCS) with 24 h lead time. The model proposed in this paper is a regularized extreme learning machine (ELM) ensemble using bagging. The method which turns the original problem into quadratic programming (QP) problem is proposed in this paper to solve lasso and elastic net problem in ELM. The forecast error of TCTs data set is the distance between real position and forecast position. Compared with the stepwise regression method widely used in TCTs, 8.26 km accuracy improvement is obtained by our model based on the dataset with 70/1680 testing/training records. By contrast, the improvement using this model is 16.49 km based on a smaller dataset with 30/720 testing/training records. Results show that the regularized ELM bagging has a general better generalization capacity on TCTs data set.

中文翻译：

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南海热带气旋路径预测的正则化ELM Bagging模型

摘要 本文旨在提高南海（SCS）热带气旋路径（TCTs）24 h提前期的预测精度。本文提出的模型是使用 bagging 的正则化极限学习机 (ELM) 集成。本文提出了将原始问题转化为二次规划（QP）问题的方法来解决ELM中的套索和弹性网问题。TCTs 数据集的预测误差是实际位置与预测位置之间的距离。与 TCT 中广泛使用的逐步回归方法相比，我们的模型基于具有 70/1680 条测试/训练记录的数据集获得了 8.26 公里的精度提升。相比之下，基于具有 30/720 条测试/训练记录的较小数据集，使用此模型的改进为 16.49 公里。