Uncertainty modelling in Rainfall-Runoff simulations based on parallel Monte Carlo method

Title:

Uncertainty modelling in Rainfall-Runoff simulations based on parallel Monte Carlo method

Creator:

Galasowski, M., Litschmannová, M., Kuchař, Š., Podhorányi, M., and Martinovič, J.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:7b14670e-4d62-4d94-b13c-239babda5257
uuid:7b14670e-4d62-4d94-b13c-239babda5257
doi:10.14311/NNW.2015.25.014

Subject:

Rainfall-Runoff, uncertainty modelling, kernel density estimation, Monte Carlo method, and high performance computing

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2015 Volume:25 | Number:3

Harvested from:

CDK

Metadata only:

false