{"title":"New Hybrid Method to Model Extreme Rainfalls","authors":"Y. Laaroussi, Z. Guennoun, A. Amar","volume":100,"journal":"International Journal of Industrial and Manufacturing Engineering","pagesStart":339,"pagesEnd":345,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10001084","abstract":"
Modeling and forecasting dynamics of rainfall
\r\noccurrences constitute one of the major topics, which have been
\r\nlargely treated by statisticians, hydrologists, climatologists and many
\r\nother groups of scientists. In the same issue, we propose, in the
\r\npresent paper, a new hybrid method, which combines Extreme
\r\nValues and fractal theories. We illustrate the use of our methodology
\r\nfor transformed Emberger Index series, constructed basing on data
\r\nrecorded in Oujda (Morocco).
\r\nThe index is treated at first by Peaks Over Threshold (POT)
\r\napproach, to identify excess observations over an optimal threshold u.
\r\nIn the second step, we consider the resulting excess as a fractal object
\r\nincluded in one dimensional space of time. We identify fractal
\r\ndimension by the box counting. We discuss the prospect descriptions
\r\nof rainfall data sets under Generalized Pareto Distribution, assured by
\r\nExtreme Values Theory (EVT). We show that, despite of the
\r\nappropriateness of return periods given by POT approach, the
\r\nintroduction of fractal dimension provides accurate interpretation
\r\nresults, which can ameliorate apprehension of rainfall occurrences.<\/p>\r\n","references":"[1] A. mirataee, B., Montaseri, M. and Rezaei, H., \u201cAssessment of goodness\r\nof fit methods in determining the best regional probability distribution of\r\nrainfall data\u201d International Journal of Engineering-Transactions A:\r\nBasics, Vol. 27, No. 10, (2014), pp. 1537-1546.\r\n[2] Evin, G. and Favre, A.C., \u201cFurther developments of a transient Poissoncluster\r\nmodel for rainfall\u201d, Stochastic environmental research and risk\r\nassessment, vol. 27, (2013), pp. 831-847.\r\n[3] Koutsoyiannis, D., \u201cStatistics of extremes and estimation of extreme\r\nrainfall\u201d, Theoretical investigation. Hydrological Sciences Journal, vol.\r\n49, (2004), pp. 575\u2013590.\r\n[4] Ceresetti, D., Anquetin, S., Molinie, G., Leblois, E. and Creutin, J. 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