Missing data simulation inside flow rate time-series using multiple-point statistics

Fabio Oriani, Andrea Borghi, Julien Straubhaar, Grégoire Mariethoz, Philippe Renard

Publikation: Bidrag til tidsskriftArtikelForskningpeer review

18 Citationer (Scopus)

Abstrakt

The direct sampling (DS) multiple-point statistical technique is proposed as a non-parametric missing data simulator for hydrological flow rate time-series. The algorithm makes use of the patterns contained inside a training data set to reproduce the complexity of the missing data. The proposed setup is tested in the reconstruction of a flow rate time-series while considering several missing data scenarios, as well as a comparative test against a time-series model of type ARMAX. The results show that DS generates more realistic simulations than ARMAX, better recovering the statistical content of the missing data. The predictive power of both techniques is much increased when a correlated flow rate time-series is used, but DS can also use incomplete auxiliary time-series, with a comparable prediction power. This makes the technique a handy simulation tool for practitioners dealing with incomplete data sets.

OriginalsprogEngelsk
Sider (fra-til)264-276
Antal sider13
TidsskriftEnvironmental Modelling and Software
Vol/bind86
DOI
StatusUdgivet - 1 dec. 2016

Programområde

  • Programområde 2: Vandressourcer

Fingeraftryk

Dyk ned i forskningsemnerne om 'Missing data simulation inside flow rate time-series using multiple-point statistics'. Sammen danner de et unikt fingeraftryk.

Citationsformater