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Hurricane genesis modelling based on the relationship between solar activity and hurricanes

Author

Listed:
  • Yaroslav Vyklyuk

    (Bukovinian University)

  • Milan Radovanović

    (Serbian Academy of Sciences and Arts)

  • Boško Milovanović

    (Serbian Academy of Sciences and Arts)

  • Taras Leko

    (Bukovinian University)

  • Milan Milenković

    (Serbian Academy of Sciences and Arts)

  • Zoran Milošević

    (Primary School Janko Veselinović)

  • Ana Milanović Pešić

    (Serbian Academy of Sciences and Arts)

  • Dejana Jakovljević

    (Serbian Academy of Sciences and Arts)

Abstract

The work examines the potential causative link between the flow of charged particles that are coming from the Sun and hurricanes. For establishing eventual link, the method of correlation analysis is applied, but with the phase shift of 0–5 days. There are nine parameters which are observed as an input, and daily values of the hurricane phenomenon are observed as an output in the period May–October 1999–2013. The results that have been obtained are considerably weak, leading to the need of applying the method of nonlinear analysis. The R/S analysis was conducted to determine the degree of randomness for time series of input and output parameters. The calculated Hurst index of variables X 4–X 9 is persistent (0.71–0.96). That allows us to conclude that the dynamics of these time series is heavily dependent on the same factors or on each other. Unlike the previous index, we have concluded that the behavior of high-energy protons (X 1–X 3) and the number of hurricane time series are completely stochastic. The problem of finding hidden dependencies in large databases refers to problems of data mining. The Sugeno function of zero order was selected as a method of output fuzzy system. Bearing in mind the available equipment, the models had to be shortened to the phase shift of 0–3 days. The “brute-force attack” method was used to find the most significant factors from all data. Within the experiments, six input factors were calculated which became the basis for building the final ANFIS models. These models can predict 22–26 % of the hurricanes.

Suggested Citation

  • Yaroslav Vyklyuk & Milan Radovanović & Boško Milovanović & Taras Leko & Milan Milenković & Zoran Milošević & Ana Milanović Pešić & Dejana Jakovljević, 2017. "Hurricane genesis modelling based on the relationship between solar activity and hurricanes," 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. 85(2), pages 1043-1062, January.
  • Handle: RePEc:spr:nathaz:v:85:y:2017:i:2:d:10.1007_s11069-016-2620-6
    DOI: 10.1007/s11069-016-2620-6
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    References listed on IDEAS

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    1. Masoomeh Mirrashid, 2014. "Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm," 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. 74(3), pages 1577-1593, December.
    2. Shahram Kaboodvandpour & Jamil Amanollahi & Samira Qhavami & Bakhtiyar Mohammadi, 2015. "Assessing the accuracy of multiple regressions, ANFIS, and ANN models in predicting dust storm occurrences in Sanandaj, Iran," 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. 78(2), pages 879-893, September.
    3. Bagher Shirmohammadi & Hamidreza Moradi & Vahid Moosavi & Majid Semiromi & Ali Zeinali, 2013. "Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran)," 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. 69(1), pages 389-402, October.
    4. Phuoc Nguyen & Lloyd Chua & Lam Son, 2014. "Flood forecasting in large rivers with data-driven models," 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. 71(1), pages 767-784, March.
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    Cited by:

    1. Slavica Malinović-Milićević & Milan M. Radovanović & Sonja D. Radenković & Yaroslav Vyklyuk & Boško Milovanović & Ana Milanović Pešić & Milan Milenković & Vladimir Popović & Marko Petrović & Petro Syd, 2023. "Application of Solar Activity Time Series in Machine Learning Predictive Modeling of Precipitation-Induced Floods," Mathematics, MDPI, vol. 11(4), pages 1-20, February.
    2. Aleksandra Nina & Vladimir A. Srećković & Milan Radovanović, 2019. "Multidisciplinarity in Research of Extreme Solar Energy Influences on Natural Disasters," Sustainability, MDPI, vol. 11(4), pages 1-6, February.
    3. Lijie Zhang & Huiyun Zhu & Jiancheng Liu, 2021. "Characteristics of tropical cyclones formed in the Eastern Pacific Northwest," 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. 106(3), pages 2619-2633, April.
    4. Tamara Lukić & Jelena Dunjić & Bojan Đerčan & Ivana Penjišević & Saša Milosavljević & Milka Bubalo-Živković & Milica Solarević, 2018. "Local Resilience to Natural Hazards in Serbia. Case Study: The West Morava River Valley," Sustainability, MDPI, vol. 10(8), pages 1-16, August.

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