Water Science and Engineering 2010, 3(3) 269-281 DOI:   10.3882/j.issn.1674-2370.2010.03.003  ISSN: 1674-2370 CN: 32-1785/TV

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Keywords
Time Series Modeling
Jordan-Elman Artificial Neural Networks
Streamflow
Forecasting
Authors
SHALAMU -abudu
CHUNLIANG -cui
KAISEER -abudukadeer
PubMed
Article by Shalamu,.A
Article by Chunliang,.C
Article by Kaiseer,.A

Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River, China

Shalamu ABUDU1, 2, Chun-liang CUI1, James Phillip KING2, Kaiser ABUDUKADEER3

1. Xinjiang Water Resources Research Institute, Urumqi 830049, P. R. China
2. Civil Engineering Department, New Mexico State University, NM 88001, USA
3. Xinjiang Water Resources Bureau, Urumqi 830000, P. R. China

Abstract

This paper presented the application of autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA) and Jordan-Elman artificial neural networks (ANN) models in forecasting monthly streamflow in the Kizil River, Xinjiang, China. Two different types of monthly streamflow data (original and deseasonalized data) were used to develop time series and Jordan-Elman neural networks forecasting models using previous flow conditions as predictors. The one-month-ahead forecasting performance of all models for testing period (1998-2005) were compared using average monthly flow of Kalabeili Gaging Station on Kizil River, Xinjiang, China. The Jordan-Elman ANN models using previous flow conditions as inputs resulted no significant improvement in one-month-ahead forecasts over time series models. The results of this study suggested that simple time series models (ARIMA and SARIMA) models could be used in one-month-ahead streamflow forecasting at the study site with a simple, explicit model structure and similar model performance as the Jordan-Elman neural networks models.

Keywords Time Series Modeling   Jordan-Elman Artificial Neural Networks   Streamflow   Forecasting  
Received 2010-05-24 Revised 2010-07-09 Online: 2010-09-27 
DOI: 10.3882/j.issn.1674-2370.2010.03.003
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Corresponding Authors: Shalamu ABUDU
Email: shalamu3@sina.com
About author:

References:

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