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Information Journal Paper

Title

STREAMFLOW FORECASTING USING NEURO-FUZZY AND TIME SERIES METHODS

Pages

  21-30

Abstract

 Simulation of river flow in order to understand the river yield in the future is one of the important and practical issues in water resource management. In this study, monthly discharge of Taleghan river in Glinak stations at one step proceeding were forecasted using Artificial Intelligent (Artificial Neural Network MLP, ANFIS with Grid Partition and Subtractive Clustering) and TIME SERIEs methods. Two inputs including raw discharge data and de-seasonalised discharge data were used for different models. For TIME SERIEs models, ARIMA (3,0,0) (0,1,1) were selected as suitable model. The optimum structure in Artificial Intelligence method after pre-processing was determined using input and output data based on trial and error, and then, using the optimum structure, the streamflow discharge was forecasted. After the output of each single model was obtained, the structure of hybrid models were determined. The results showed hybrid methods 3 and 2 have the best application and TIME SERIEs model has better results than Artificial Intelligent methods.

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  • Cite

    APA: Copy

    FATHABADI, A.A.H., SALAJEGHEH, ALI, & MAHDAVI, MOHAMMAD. (2009). STREAMFLOW FORECASTING USING NEURO-FUZZY AND TIME SERIES METHODS. IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING, 2(5), 21-30. SID. https://sid.ir/paper/134637/en

    Vancouver: Copy

    FATHABADI A.A.H., SALAJEGHEH ALI, MAHDAVI MOHAMMAD. STREAMFLOW FORECASTING USING NEURO-FUZZY AND TIME SERIES METHODS. IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING[Internet]. 2009;2(5):21-30. Available from: https://sid.ir/paper/134637/en

    IEEE: Copy

    A.A.H. FATHABADI, ALI SALAJEGHEH, and MOHAMMAD MAHDAVI, “STREAMFLOW FORECASTING USING NEURO-FUZZY AND TIME SERIES METHODS,” IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING, vol. 2, no. 5, pp. 21–30, 2009, [Online]. Available: https://sid.ir/paper/134637/en

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