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Information Journal Paper

Title

Comparing PAR and MPAR Models to Modeling the Monthly River Flow Rates Time Series Under the Influence of Meteorological Factors, The Case Study: Nazloochai River

Pages

  183-191

Abstract

 For over three decades, hydrologists were recommended Multivariate Models to describe and Modeling complex hydrology data. While recently the Multivariate Models in hydrology is discussed. In Multivariate Models, the Modeling and predicting various parameters can improve by involving other factors. In this study, using both univariate and multivariate Periodic ARMA models for Modeling monthly discharge in Nazloochaei River in West Azerbaijan Province during the period of 1962-2011 were compared. The results of evaluation and verification models showed that the multivariate Periodic ARMA models by involving the climatic parameters such as temperature and precipitation of the basin, the more accurately than univariate Periodic ARMA models. Also the result showed that the selected models, maximum and minimum points of discharge to the appropriate model.

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    APA: Copy

    Nazeri Tahrudi, Mohammad. (2018). Comparing PAR and MPAR Models to Modeling the Monthly River Flow Rates Time Series Under the Influence of Meteorological Factors, The Case Study: Nazloochai River. JOURNAL OF MODELING IN ENGINEERING, 16(52 ), 183-191. SID. https://sid.ir/paper/358180/en

    Vancouver: Copy

    Nazeri Tahrudi Mohammad. Comparing PAR and MPAR Models to Modeling the Monthly River Flow Rates Time Series Under the Influence of Meteorological Factors, The Case Study: Nazloochai River. JOURNAL OF MODELING IN ENGINEERING[Internet]. 2018;16(52 ):183-191. Available from: https://sid.ir/paper/358180/en

    IEEE: Copy

    Mohammad Nazeri Tahrudi, “Comparing PAR and MPAR Models to Modeling the Monthly River Flow Rates Time Series Under the Influence of Meteorological Factors, The Case Study: Nazloochai River,” JOURNAL OF MODELING IN ENGINEERING, vol. 16, no. 52 , pp. 183–191, 2018, [Online]. Available: https://sid.ir/paper/358180/en

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