Regional Typhoon Track Prediction Using Ensemble k-Nearest Neighbor Machine Learning in the GIS Environment

被引:10
|
作者
Tamamadin, Mamad [1 ,2 ]
Lee, Changkye [3 ]
Kee, Seong-Hoon [1 ,3 ]
Yee, Jurng-Jae [1 ,3 ]
机构
[1] Dong A Univ, Dept ICT Integrated Ocean Smart Cities Engn, Busan 49315, South Korea
[2] Inst Teknol Bandung, Dept Meteorol, Bandung 40132, Indonesia
[3] Dong A Univ, Univ Core Res Ctr Disaster free Safe Ocean City C, Busan 49315, South Korea
基金
新加坡国家研究基金会;
关键词
k-Nearest Neighbor; GIS processing; machine learning; similarity; typhoon track prediction;
D O I
10.3390/rs14215292
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper presents a novel approach for typhoon track prediction that potentially impacts a region using ensemble k-Nearest Neighbor (k-NN) in a GIS environment. In this work, the past typhoon tracks are zonally split into left and right classes by the current typhoon track and then grouped as an ensemble member containing three (left-center-right) typhoons. The proximity of the current typhoon to the left and/or right class is determined by using a supervised classification k-NN algorithm. The track dataset created from the current and similar class typhoons is trained by using the supervised regression k-NN to predict current typhoon tracks. The ensemble averaging is performed for all typhoon track groups to obtain the final track prediction. It is found that the number of ensemble members does not necessarily affect the accuracy; the determination of similarity at the beginning, however, plays an important key role. A series of tests yields that the present method is able to produce a typhoon track prediction with a fast simulation time, high accuracy, and long duration.
引用
收藏
页数:22
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