计算机与现代化

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LSTM网络在台风路径预测中的应用

  

  1. (西南交通大学数学学院,四川成都611756)
  • 收稿日期:2018-12-03 出版日期:2019-05-14 发布日期:2019-05-14
  • 作者简介:徐高扬(1991-),男,河南洛阳人,硕士研究生,研究方向:时空数据分析,E-mail: gaoyangxu@163.com; 刘姚(1992-),女,硕士研究生,研究方向:智能信息处理。
  • 基金资助:
    国家自然科学基金资助项目(51778546, 51578471); 四川省青年基金资助项目(2016JQ0005)

Application of LSTM in Typhoon Path Prediction

  1. (School of Mathematics, Southwest Jiaotong University, Chengdu 611756, China)
  • Received:2018-12-03 Online:2019-05-14 Published:2019-05-14

摘要: 台风路径实质为二维平面上一段曲线,根据2条台风路径曲线的相似度可以判断其数值相似和形态相似,由此利用动态规整算法可以从历史台风数据库筛选出与目标台风相似的台风路径。同时考虑到台风路径信息的时间关联性,提出了长短时记忆网络预测模型。利用历史台风的经纬度信息,预测台风未来6小时位置信息,对比传统基于相似度预测台风路径方法,长短时记忆模型能够有效提高台风路径预测精度,模型更加稳定高效。

关键词: 相似度, 动态规整, 长短时记忆网络, 路径预测

Abstract: The typhoon paths are essentially curves on a two-dimensional plane. According to the similarity of the two typhoon path curves, the numerical similarity and morphology similarity can be judged. Therefore, the dynamic time warping algorithm can be used to select the typhoon path similar to the target typhoon from the historical typhoon database. Meanwhile, considering the time correlation of typhoon path information, we put forword a long short-term memory model. The paper uses the latitude and longitude information of historical typhoon to predict the position information of the typhoon in the next 6 hours. Compared with the traditional method of predicting typhoon path by similarity, the long short-term memory model can effectively improve the prediction accuracy of typhoon paths, and the model is more stable and efficient.

Key words: similarity, DTW, LSTM, trajectories predicting

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