River flow modelling using nonparametric functional data analysis
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River flow modelling using nonparametric functional data analysisData
2018Cita bibliográfica
Quintela-del-Río, A. and Francisco-Fernández, M. (2018), River flow modelling using nonparametric functional data analysis. J Flood Risk Management, 11: S902-S915. https://doi.org/10.1111/jfr3.12282
Resumo
[Abstract]: Time series and extreme value analyses are two statistical approaches usually
applied to study hydrological data. Classical techniques, such as autoregressive
integrated moving-average models (in the case of mean flow predictions), and
parametric generalised extreme value fits and nonparametric extreme value
methods (in the case of extreme value theory) have been usually employed in this
context. In this article, nonparametric functional data methods are used to perform
mean monthly flow predictions and extreme value analysis, which are
important for flood risk management. These are powerful tools that take advantage
of both, the functional nature of the data under consideration and the flexibility
of nonparametric methods, providing more reliable results. Therefore, they
can be useful to prevent damage caused by floods and to reduce the likelihood
and/or the impact of floods in a specific location. The nonparametric functional
approaches are applied to flow samples of two rivers in the United States. In this
way, monthly mean flow is predicted and flow quantiles in the extreme value
framework are estimated using the proposed methods. Results show that the
nonparametric functional techniques work satisfactorily, generally outperforming
the behaviour of classical parametric and nonparametric estimators in both
settings.
Palabras chave
Extreme values
Forecasting
Functional data
Nonparametric estimation
River flow
Forecasting
Functional data
Nonparametric estimation
River flow
Versión do editor
Dereitos
Atribución 3.0 España
ISSN
1753-318X