Efficient methods of automatic calibration for rainfall-runoff modelling in the Floreon+ system

Title:

Efficient methods of automatic calibration for rainfall-runoff modelling in the Floreon+ system

Creator:

Theuer, M., Vavřík, R., Vondrák, V., Kuchař, Š., Šír, B., and Portero, A.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:41b82e5c-7742-4b9b-8f75-7c610e6f07bc
uuid:41b82e5c-7742-4b9b-8f75-7c610e6f07bc
doi:10.14311/NNW.2017.27.022

Subject:

rainfall-runoff modelling, automatic calibration, optimization algorithms, genetic algorithms, and parrallel implementation

Type:

model:article and TEXT

Format:

jiný

Description:

Calibration of rainfall-runoff model parameters is an inseparable part of hydrological simulations. To achieve more accurate results of these simulations, it is necessary to implement an efficient calibration method that provides sufficient refinement of the model parameters in a reasonable time frame. In order to perform the calibration repeatedly for large amount of data and provide results of calibrated model simulations for the flood warning process in a short time, the method also has to be automated. In this paper, several local and global optimization methods are tested for their efficiency. The main goal is to identify the most accurate method for the calibration process that provides accurate results in an operational time frame (typically less than 1 hour) to be used in the flood prediction Floreon\textsuperscript{+} system. All calibrations were performed on the measured data during the rainfall events in 2010 in the Moravian-Silesian region (Czech Republic) using our in-house rainfall-runoff model.

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 | 2017 Volume:27 | Number:4

Harvested from:

CDK

Metadata only:

false