Streamflow forecasting of Astore River with Seasonal Autoregressive Integrated Moving Average model

  • Rana Muhammad Adnan School of Hydropower and Information Engineering, Huazhong University of Science & Technology, 430074 Wuhan, China
  • Xiaohui Yuan School of Hydropower and Information Engineering, Huazhong University of Science & Technology, 430074 Wuhan, China
  • Ozgur Kisi Center for Interdisciplinary Research, International Black Sea University, Tbilisi, Georgia
  • Yanbin Yuan School of Resource and Environmental Engineering, Wuhan University of Technology, China

Abstract

Simulation of streamflow is one of important factors in water utilization. In this paper, a linear statistical model i.e. Seasonal Autoregressive Integrated Moving Average model (SARIMA) is applied for modeling streamflow data of Astore River (1974 – 2010). On the basis of minimum Akaike Information Criteria Corrected (AICc) and Bayesian Information Criteria (BIC) values, the best model from different model structures has been identified. For testing period (2004-2010), the prediction accuracy of selected SARIMA model in comparison of auto regressive (AR) is evaluated on basis of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R2 ). The results show that SARIMA performed better than AR model and can be used in streamflow forecasting at the study site.

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Published
2017-04-30
How to Cite
Adnan, R. M., Yuan, X., Kisi, O., & Yuan, Y. (2017). Streamflow forecasting of Astore River with Seasonal Autoregressive Integrated Moving Average model. European Scientific Journal, ESJ, 13(12), 145. https://doi.org/10.19044/esj.2017.v13n12p145