Abstract
The method of analysis of multisource data statistics is proposed for extreme forecasting and meteorological disaster risk analysis. This method is based on nonlinear kernel-based principal component algorithm (KPCA) modified according to specific of data: socioeconomic, disaster statistics, climatic, ecological, infrastructure distribution. Using this method the set of long-term regional statistics of disasters distributions and variations of economic activity has been analyzed. On these examples the method of obtaining of the spatially and temporally normalized and regularized distributions of the parameters investigated has been demonstrated. Method of extreme distribution assessment based on analysis of meteorological measurements should be described. Analysis of regional climatic parameters distribution allows to estimate the probability of extremes (both on seasonal and annual scales) toward mean climatic values change. The way to coherent risk measures assessment based on coupled analysis of multidimensional multivariate distributions should be described. Using the method of assessment of complex risk measures on the base of coupled analysis of multidimensional multivariate distributions of data the regional risk of climatic, meteorological, and hydrological disasters were estimated basing on kernel copula semi-parametric algorithm.
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EU-27: Austria, Belgium, Bulgaria, Cyprus, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, and the United Kingdom.
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Kostyuchenko, Y.V. (2015). Geostatistics and Remote Sensing for Extremes Forecasting and Disaster Risk Multiscale Analysis. In: Kadry, S., El Hami, A. (eds) Numerical Methods for Reliability and Safety Assessment. Springer, Cham. https://doi.org/10.1007/978-3-319-07167-1_16
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