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Dudziński Marcin (Szkoła Główna Gospodarstwa Wiejskiego w Warszawie), Furmańczyk Konrad (Szkoła Główna Gospodarstwa Wiejskiego w Warszawie)
The Quantile Estimation of the Maxima of Sea Levels
Metody Ilościowe w Badaniach Ekonomicznych / Szkoła Główna Gospodarstwa Wiejskiego, 2011, vol. 12(XII), nr 1, s. 37-52, wykr., tab., bibliogr. 5 poz.
Quantitative Methods in Economics
Słowa kluczowe
Funkcja ufności, Zastosowanie matematyki
Belief function, Applications of mathematics
The hydrological modeling has become an intensively studied subject in recent years. One of the most significant problems concerning this issue is to provide the mathematical and statistical tools, which allow to forecast extreme hydrological events, such as severe sea or river floodings. The extreme events on water have huge social and economic impact on the affected areas. Due to these reasons, each country has to protect itself against the flood danger, and consequently, the designing of reliable flood defences is of great importance to the safety of the region. For example, the sea dikes along the Dutch coastline are designed to withstand floods, which may occur once every 10 000 years. It means that the height of the dike is determined in such a way that the probability of the event that there is a flood in a given year equals 10-4. The computation of such the height level requires the estimation of the corresponding quantiles of the distributions of certain maxima of sea levels. In our paper, we present the procedures, which lead to the estimation of such the quantiles. We are mainly concerned with the interval estimation; in this context, we present the frequentistic and Bayesian approaches in constructing the desired confidence intervals. (original abstract)
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Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Pełny tekst
  1. Katz R.W., Parlange M.B., Naveau P. (2002) Statistics of extremes in hydrology, Advances in water resources, 25, pp. 1287-1304.
  2. Knox J.C., Kundzewicz Z.W. (1997) Extreme hydrological events, palaeo-information and climate change, Hydrological Sciences-Journal-des Sciences Hydrologiques, 42, pp. 765-779.
  3. Shiau J.T. (2003) Return period of bivariate distributed extreme hydrological events, Stochastic environmental research and risk assessment, 17, pp. 42-57.
  4. Van Gelder P.H.A.J.M. (1996) A Bayesian analysis of extreme water levels along the Dutch coast using flood historical data, Stochastic Hydraulics, 7, pp. 243-251.
  5. Van Gelder P.H.A.J.M., Roos A., Tonneijck M.R. (1995) On the probabilistic design of dikes in the Netherlands, Applications of Statistics and Probability, 3, pp. 1505-1508.
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