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
相关论文
共 50 条
  • [21] FML-kNN: scalable machine learning on Big Data using k-nearest neighbor joins
    Chatzigeorgakidis, Georgios
    Karagiorgou, Sophia
    Athanasiou, Spiros
    Skiadopoulos, Spiros
    JOURNAL OF BIG DATA, 2018, 5 (01)
  • [22] PREDICTION OF BREAST CANCER USING K-NEAREST NEIGHBOUR: A SUPERVISED MACHINE LEARNING ALGORITHM
    Pandey, S.
    Sharma, A.
    Siddiqui, M. K.
    Singla, D.
    Vanderpuye-Orgle, J.
    VALUE IN HEALTH, 2020, 23 : S1 - S1
  • [23] An analysis on two different data sets by using ensemble of k-nearest neighbor classifiers
    Ramli, Nor Azuana
    Ismail, Mohd Tahir
    Wooi, Hooy Chee
    WSEAS Transactions on Mathematics, 2014, 13 : 780 - 789
  • [24] An Optimal K-Nearest Neighbor for Weather Prediction Using Whale Optimization Algorithm
    Moorthy, Rajalakshmi Shenbaga
    Parameshwaran, Pabitha
    INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING, 2022, 13 (01)
  • [25] Real Value Prediction of Solvent Accessibility by Using the k-Nearest Neighbor Method
    Lee, Julian
    JOURNAL OF THE KOREAN PHYSICAL SOCIETY, 2009, 54 (01) : 1 - 6
  • [26] Development of a Crash Risk Prediction Model Using the k-Nearest Neighbor Algorithm
    Kang, Min Ji
    Kwon, Oh Hoon
    Park, Shin Hyoung
    ADVANCED MULTIMEDIA AND UBIQUITOUS ENGINEERING, MUE/FUTURETECH 2018, 2019, 518 : 835 - 840
  • [27] Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method
    Sim, J
    Kim, SY
    Lee, J
    BIOINFORMATICS, 2005, 21 (12) : 2844 - 2849
  • [28] Melanoma prediction using k-nearest neighbor and LEM2 algorithms
    Grzymala-Busse, JW
    Hippe, ZS
    INTELLIGENT INFORMATION SYSTEMS 2001, 2001, : 43 - 55
  • [29] Design Exploration of ASIP Architectures for the K-Nearest Neighbor Machine-Learning Algorithm
    Jamma, Dunia
    Ahmed, Omar
    Areibi, Shawki
    Grewal, Gary
    Molloy, Nicholas
    2016 28TH INTERNATIONAL CONFERENCE ON MICROELECTRONICS (ICM 2016), 2016, : 57 - 60
  • [30] Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble
    Sun, Wei
    Lv, Ying
    Li, Gongchen
    Chen, Yumin
    WATER, 2020, 12 (01)