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  • The presented paper deals with a stochastic simulation of snow cover. This study aims to find the best settings of a stochastic simulation to be able to determine the parameters of the snow cover for any point of a given territory. Next, basic statistical analyses of parameters are documented, including an analysis of relationships between the snow parameters and altitude, slope and aspect. Most current methods of spatial interpolation and multifactor evaluation are based on the weighted regression relationships. That leads to smooth results and degrades our ability to properly evaluate the existence and the probability of extreme situations and their impact on the research problem. Neither alternative techniques use neural networks to bring major improvements.
  • The presented paper deals with a stochastic simulation of snow cover. This study aims to find the best settings of a stochastic simulation to be able to determine the parameters of the snow cover for any point of a given territory. Next, basic statistical analyses of parameters are documented, including an analysis of relationships between the snow parameters and altitude, slope and aspect. Most current methods of spatial interpolation and multifactor evaluation are based on the weighted regression relationships. That leads to smooth results and degrades our ability to properly evaluate the existence and the probability of extreme situations and their impact on the research problem. Neither alternative techniques use neural networks to bring major improvements. (en)
Title
  • Stochastic Simulation of Snow Cover
  • Stochastic Simulation of Snow Cover (en)
skos:prefLabel
  • Stochastic Simulation of Snow Cover
  • Stochastic Simulation of Snow Cover (en)
skos:notation
  • RIV/61989100:27350/11:86078981!RIV12-MSM-27350___
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  • 232428
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  • RIV/61989100:27350/11:86078981
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  • stochastic simulation; interpolation (en)
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  • [79773535F722]
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  • Barcelona
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  • Barcelona
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  • Proceedings of SIMUL 2011
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  • Orlíková, Lucie
  • Průšová, Markéta
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number of pages
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  • International Academy, Research and Industry Association
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  • 978-1-61208-169-4
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  • 27350
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