UNCERTAINTY MODELLING IN RAINFALL-RUNOFF SIMULATIONS BASED ON PARALLEL MONTE CARLO METHOD

This article describes statistical evaluation of the computational model for precipitation forecast and proposes a method for uncertainty modelling of rainfall-runoff models in the Floreon^+ system based on this evaluation. The Monte-Carlo simulation method is used for estimating possible river disc...

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Vydáno v:Neural Network World Ročník 25; číslo 3; s. 267 - 286
Hlavní autoři: Golasowski, M, Litschmannova, M, Kuchar, S, Podhorányi, M, Martinovic, J
Médium: Journal Article
Jazyk:angličtina
Vydáno: Prague Institute of Information and Computer Technology 01.01.2015
Czech Technical University in Prague, Faculty of Transportation Sciences
Témata:
Computer simulation
Discharge
High performance computing
Mathematical models
Modelling
Monte Carlo simulation
Precipitation
Rain
Rivers
Runoff
Uncertainty
Monte Carlo method
kernel density estimation
high performance computing
uncertainty modelling
Rainfall-Runoff
ISSN:1210-0552, 2336-4335
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Shrnutí:This article describes statistical evaluation of the computational model for precipitation forecast and proposes a method for uncertainty modelling of rainfall-runoff models in the Floreon^+ system based on this evaluation. The Monte-Carlo simulation method is used for estimating possible river discharge and provides several confidence intervals that can support the decisions in operational disaster management. Experiments with other parameters of the model and their influence on final river discharge are also discussed.
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ISSN:1210-0552
2336-4335
DOI:10.14311/NNW.2015.25.014