Articles | Volume 21, issue 6
https://doi.org/10.5194/npg-21-1159-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/npg-21-1159-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
01 Dec 2014
Research article |
| 01 Dec 2014
An improved ARIMA model for precipitation simulations
H. R. Wang
College of Water Sciences, Key Laboratory for Water and Sediment Sciences Ministry of Education, Beijing Normal University, 19 Xinjiekouwai Street, Beijing, 100875, China
College of Water Sciences, Key Laboratory for Water and Sediment Sciences Ministry of Education, Beijing Normal University, 19 Xinjiekouwai Street, Beijing, 100875, China
X. Lin
College of Mathematic Sciences, Beijing Normal University, 19 Xinjiekouwai Street, Beijing, 100875, China
J. Kang
College of Mathematic Sciences, Beijing Normal University, 19 Xinjiekouwai Street, Beijing, 100875, China
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- Development of Precipitation Forecast Model Based on Artificial Intelligence and Subseasonal Clustering L. Parviz & K. Rasouli 10.1061/(ASCE)HE.1943-5584.0001862
- Rainfall forecasting in arid regions in response to climate change using ARIMA and remote sensing H. F. Abd-Elhamid et al. 10.1080/19475705.2024.2347414
- Long-Term Stochastic Modeling of Monthly Streamflow in River Nile S. Abdelaziz et al. 10.3390/su15032170
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- Temporal Hierarchical Reconciliation for Consistent Water Resources Forecasting Across Multiple Timescales: An Application to Precipitation Forecasting M. Jahangir & J. Quilty 10.1029/2021WR031862
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Latest update: 27 Dec 2024
Short summary
This paper presents an improvement on the conventional ARIMA model for precipitation time-series forecast. The precipitation time series of 12 months is first classified into several clusters. The maxima, minima, and truncation means of each cluster are then predicted using the improved ARIMA models, which are further used to predict the monthly precipitation through a set of regression models. A case study demonstrates that the present approach could increase the forecast accuracy by 21%.
This paper presents an improvement on the conventional ARIMA model for precipitation time-series...
