IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v112y2022i1d10.1007_s11069-021-05195-6.html
   My bibliography  Save this article

A quantitative method for the similarity assessment of typhoon tracks

Author

Listed:
  • Yangchen Di

    (Nanjing University of Information Science & Technology)

  • Mingyue Lu

    (Nanjing University of Information Science & Technology)

  • Min Chen

    (Nanjing Normal University
    State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province)
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

  • Zhangjian Chen

    (Zhejiang Academy of Surveying and Mapping)

  • Zaiyang Ma

    (Nanjing Normal University
    State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province)
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application)

  • Manzhu Yu

    (The Pennsylvania State University)

Abstract

Typhoons are one of the most dangerous types of natural hazards; they are always developed in the western and southwestern Pacific Ocean and pose economic and human security threats to the Pacific Rim annually. Therefore, many scholars in related fields devote themselves to finding an effective way to analyze and forecast typhoon tracks to prevent disasters. Similarity analysis of typhoon tracks can provide great help for typhoon prediction. In this paper, a model for typhoon similarity analysis is proposed to effectively measure and quantify the similarity between two historical typhoon tracks based on the dynamic time warping algorithm, in which five typhoon elements—namely, longitude, latitude, central pressure, expanded Beaufort scale, and movement speed—are integrated to derive a final similarity percentage indicating the similarity level. At the end of this paper, case studies concerning historical typhoons and the ongoing Typhoon 202,106 In-Fa are also conducted to verify the validity and effectiveness of the proposed model. The results show that the proposed model can effectively provide a quantitative similarity of two typhoon tracks when functioning well on ongoing typhoons with a cutoff rule and supplying promising support for typhoon prediction simultaneously.

Suggested Citation

  • Yangchen Di & Mingyue Lu & Min Chen & Zhangjian Chen & Zaiyang Ma & Manzhu Yu, 2022. "A quantitative method for the similarity assessment of typhoon tracks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(1), pages 587-602, May.
  • Handle: RePEc:spr:nathaz:v:112:y:2022:i:1:d:10.1007_s11069-021-05195-6
    DOI: 10.1007/s11069-021-05195-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-021-05195-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11069-021-05195-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Giorgino, Toni, 2009. "Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i07).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Amato, Umberto & Antoniadis, Anestis & De Feis, Italia & Goude, Yannig & Lagache, Audrey, 2021. "Forecasting high resolution electricity demand data with additive models including smooth and jagged components," International Journal of Forecasting, Elsevier, vol. 37(1), pages 171-185.
    2. Mastroeni, Loretta & Mazzoccoli, Alessandro & Quaresima, Greta & Vellucci, Pierluigi, 2021. "Decoupling and recoupling in the crude oil price benchmarks: An investigation of similarity patterns," Energy Economics, Elsevier, vol. 94(C).
    3. Christoph J. Borner & Ingo Hoffmann & Jonas Krettek & Lars M. Kurzinger & Tim Schmitz, 2021. "Bitcoin: Like a Satellite or Always Hardcore? A Core-Satellite Identification in the Cryptocurrency Market," Papers 2105.12336, arXiv.org.
    4. Hanjo Odendaal & Monique Reid & Johann F. Kirsten, 2020. "Media‐Based Sentiment Indices as an Alternative Measure of Consumer Confidence," South African Journal of Economics, Economic Society of South Africa, vol. 88(4), pages 409-434, December.
    5. Sokhna Dieng & Pierre Michel & Abdoulaye Guindo & Kankoe Sallah & El-Hadj Ba & Badara Cissé & Maria Patrizia Carrieri & Cheikh Sokhna & Paul Milligan & Jean Gaudart, 2020. "Application of Functional Data Analysis to Identify Patterns of Malaria Incidence, to Guide Targeted Control Strategies," IJERPH, MDPI, vol. 17(11), pages 1-23, June.
    6. Beste Hamiye Beyaztas & Ufuk Beyaztas & Soutir Bandyopadhyay & Wei-Min Huang, 2018. "New and Fast Block Bootstrap-Based Prediction Intervals for GARCH(1,1) Process with Application to Exchange Rates," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 168-194, February.
    7. Yiyu Li & Qingxu Huang & Ling Zhang & Jian Li & Yingfei Sui & Weichen Zhang, 2022. "Dynamics of Urban Land per Capita in China from 2000 to 2016," Land, MDPI, vol. 12(1), pages 1-16, December.
    8. Debarsy, Nicolas & Dossougoin, Cyrille & Ertur, Cem & Gnabo, Jean-Yves, 2018. "Measuring sovereign risk spillovers and assessing the role of transmission channels: A spatial econometrics approach," Journal of Economic Dynamics and Control, Elsevier, vol. 87(C), pages 21-45.
    9. MacPherson, Brian & Scott, Ryan & Gras, Robin, 2023. "Using individual-based modelling to investigate a pluralistic explanation for the prevalence of sexual reproduction in animal species," Ecological Modelling, Elsevier, vol. 475(C).
    10. Christoph J. Börner & Ingo Hoffmann & Jonas Krettek & Tim Schmitz, 2022. "Bitcoin: like a satellite or always hardcore? A core–satellite identification in the cryptocurrency market," Journal of Asset Management, Palgrave Macmillan, vol. 23(4), pages 310-321, July.
    11. Alexandre Lucas & Salvador Carvalhosa, 2022. "Renewable Energy Community Pairing Methodology Using Statistical Learning Applied to Georeferenced Energy Profiles," Energies, MDPI, vol. 15(13), pages 1-16, June.
    12. Charlie Lindgren & Sven-Olov Daunfeldt & Niklas Rudholm & Siril Yella, 2021. "Is intertemporal price discrimination the cause of price dispersion in markets with low search costs?," Applied Economics Letters, Taylor & Francis Journals, vol. 28(11), pages 968-971, June.
    13. Jia Luo & Jingying Huang & Jiancheng Ma & Siyuan Liu, 2024. "Application of self-attention conditional deep convolutional generative adversarial networks in the fault diagnosis of planetary gearboxes," Journal of Risk and Reliability, , vol. 238(2), pages 260-273, April.
    14. Miljkovic, Dragan & Vatsa, Puneet, 2023. "On the linkages between energy and agricultural commodity prices: A dynamic time warping analysis," International Review of Financial Analysis, Elsevier, vol. 90(C).
    15. Chainarong Amornbunchornvej & Elena Zheleva & Tanya Berger-Wolf, 2020. "Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis," Papers 2002.00208, arXiv.org, revised Jun 2020.
    16. Timmermans, Catherine & von Sachs, Rainer, 2013. "BAGIDIS: Statistically investigating curves with sharp local patterns using a new functional measure of dissimilarity," LIDAM Discussion Papers ISBA 2013031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    17. De Gregorio, Alessandro & Maria Iacus, Stefano, 2010. "Clustering of discretely observed diffusion processes," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 598-606, February.
    18. Chong Guan & Wenting Liu & Jack Yu-Chao Cheng, 2022. "Using Social Media to Predict the Stock Market Crash and Rebound amid the Pandemic: The Digital ‘Haves’ and ‘Have-mores’," Annals of Data Science, Springer, vol. 9(1), pages 5-31, February.
    19. Alexandra I. Klimenko & Diana A. Vorobeva & Sergey A. Lashin, 2023. "A New Visualization and Analysis Method for a Convolved Representation of Mass Computational Experiments with Biological Models," Mathematics, MDPI, vol. 11(12), pages 1-19, June.
    20. Olufolajimi Oke & Kavi Bhalla & David C. Love & Sauleh Siddiqui, 2018. "Spatial associations in global household bicycle ownership," Annals of Operations Research, Springer, vol. 263(1), pages 529-549, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:112:y:2022:i:1:d:10.1007_s11069-021-05195-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.