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

详细信息    查看全文

• 作者：Yaroslav Vyklyuk ; Milan Radovanović ; Boško Milovanović ; Taras Leko…
• 关键词：Solar activity ; Hurricanes ; Hurst index ; ANFIS models
• 刊名：Natural Hazards
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：85
• 期：2
• 页码：1043-1062
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Natural Hazards; Hydrogeology; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Civil Engineering; Environmental Management;
• 出版者：Springer Netherlands
• ISSN：1573-0840
• 卷排序：85

文摘

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 X₄–X₉ 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 (X₁–X₃) 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.