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An Improved Arima Model for Precipitation Simulations : Volume 21, Issue 6 (01/12/2014)

By Wang, H. R.

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Book Id: WPLBN0003991569
Format Type: PDF Article :
File Size: Pages 10
Reproduction Date: 2015

Title: An Improved Arima Model for Precipitation Simulations : Volume 21, Issue 6 (01/12/2014)  
Author: Wang, H. R.
Volume: Vol. 21, Issue 6
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2014
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Wang, C., Lin, X., Kang, J., & Wang, H. R. (2014). An Improved Arima Model for Precipitation Simulations : Volume 21, Issue 6 (01/12/2014). Retrieved from http://www.worldebookfair.com/


Description
Description: College of Water Sciences, Key Laboratory for Water and Sediment Sciences Ministry of Education, Beijing Normal University, 19 Xinjiekouwai Street, Beijing, 100875, China. 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%.

Summary
An improved ARIMA model for precipitation simulations

Excerpt
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