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| Titel |
An improved ARIMA model for precipitation simulations |
| VerfasserIn |
H. R. Wang, C. Wang, X. Lin, J. Kang |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 21, no. 6 ; Nr. 21, no. 6 (2014-12-01), S.1159-1168 |
| Datensatznummer |
250120956
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| Publikation (Nr.) |
 copernicus.org/npg-21-1159-2014.pdf |
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| Zusammenfassung |
| Auto regressive integrated moving average (ARIMA) models have been widely
used to calculate monthly time series data formed by interannual variations
of monthly data or inter-monthly variation. However, the influence brought
about by inter-monthly variations within each year is often ignored. An
improved ARIMA model is developed in this study accounting for both the
interannual and inter-monthly variation. In the present approach,
clustering analysis is performed first to hydrologic variable time series.
The characteristics of each class are then extracted and the correlation
between the hydrologic variable quantity to be predicted and characteristic
quantities constructed by linear regression analysis. ARIMA models are built
for predicting these characteristics of each class and the hydrologic
variable monthly values of year of interest are finally predicted using the
modeled values of corresponding characteristics from ARIMA model and the
linear regression model. A case study is conducted to predict the monthly
precipitation at the Lanzhou precipitation station in Lanzhou, 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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