<abstract xmlns="http://www.w3.org/1999/xhtml"><p> The rainfall-runoff transformation is a highly complex dynamic process and the development of fast and robust modelling instruments has always been one of the most important topics for hydrology. Over time, a significant number of hydrological models have been developed with a clear trend towards a process-based approach. The downside of these types of models is the significant amount of data required for building the model and for the calibration process: in practice, the collection of all necessary data for such models proves to be a difficult task. In order to cope with this issue, various data-driven modelling techniques have been introduced for hydrological modelling as an alternative to more traditional approaches, on the basis of their capacity of mapping out complex relationships from observation data. Having the capacity to generate meaningful mathematical structures as results, genetic programming (GP) presents a high potential for rainfall-runoff modelling as a data-driven method. Using ground and radar rainfall observation, the aim of this study is to investigate the GP technique capability for modelling the rainfall-runoff process, taking into consideration a flash-flood event.</p></abstract>

The rainfall-runoff transformation is a highly complex dynamic process and the development of fast and robust modelling instruments has always been one of the most important topics for hydrology. Over time, a significant number of hydrological models have been developed with a clear trend towards a process-based approach. The downside of these types of models is the significant amount of data required for building the model and for the calibration process: in practice, the collection of all necessary data for such models proves to be a difficult task. In order to cope with this issue, various data-driven modelling techniques have been introduced for hydrological modelling as an alternative to more traditional approaches, on the basis of their capacity of mapping out complex relationships from observation data. Having the capacity to generate meaningful mathematical structures as results, genetic programming (GP) presents a high potential for rainfall-runoff modelling as a data-driven method. Using ground and radar rainfall observation, the aim of this study is to investigate the GP technique capability for modelling the rainfall-runoff process, taking into consideration a flash-flood event.

Genetic Programming Technique Applied for Flash-Flood Modelling Using Radar Rainfall Estimates

Modelling in Civil Environmental Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The rainfall-runoff transformation is a highly complex dynamic process and the development of fast and robust modelling instruments has always been one of the...

Genetic Programming Technique Applied for Flash-Flood Modelling Using Radar Rainfall Estimates

Online veröffentlicht: 06. Feb. 2018

Seitenbereich: 27 - 38

DOI: https://doi.org/10.1515/mmce-2017-0012

Schlüsselwörter
rainfall-runoff modelling, genetic programming, data-driven models, flash-flood, radar rainfall estimates

© by Cristian Dinu

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Genetic Programming Technique Applied for Flash-Flood Modelling Using Radar Rainfall Estimates

Online veröffentlicht: 06. Feb. 2018

Seitenbereich: 27 - 38

DOI: https://doi.org/10.1515/mmce-2017-0012

Schlüsselwörterrainfall-runoff modelling, genetic programming, data-driven models, flash-flood, radar rainfall estimates

© by Cristian Dinu

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
rainfall-runoff modelling, genetic programming, data-driven models, flash-flood, radar rainfall estimates