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| Titel |
An improved ARIMA model for hydrological simulations |
| VerfasserIn |
H. R. Wang, C. Wang, X. Lin, J. Kang |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2198-5634
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 1, no. 1 ; Nr. 1, no. 1 (2014-04-30), S.841-876 |
| Datensatznummer |
250115094
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| Publikation (Nr.) |
 copernicus.org/npgd-1-841-2014.pdf |
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| Zusammenfassung |
| Auto Regressive Integrated Moving Average (ARIMA) model is often used to calculate time series
data formed by inter-annual variations of monthly data. However, the influence brought about by
inter-monthly variations within each year is ignored. Based on the monthly data classified by
clustering analysis, the characteristics of time series data are extracted. An improved ARIMA
model is developed accounting for both the inter-annual and inter-monthly variation. The
correlation between characteristic quantity and monthly data within each year is constructed by
regression analysis first. The model can be used for predicting characteristic quantity followed
by the stationary treatment for characteristic quantity time series by difference. A case study is
conducted to predict the precipitation in Lanzhou precipitation station, China, using the model,
and the results show that the accuracy of the improved model is significantly higher than the
seasonal model, with the mean residual achieving 9.41 mm and the forecast accuracy
increasing by 21%. |
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