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Information Journal Paper

Title

RIVER FLOW FORECASTING USING FUZZY INFERENCE SYSTEM (FIS) AND ADAPTIVE NEURO- FUZZY INFERENCE SYSTEM (ANFIS)

Pages

  7-14

Keywords

FUZZY INFERENCE SYSTEM (FIS)Q2
ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)Q2

Abstract

 In recent years, using FUZZY SETS THEORY in modeling of complex and uncertain hydrological phenomena has attracted research workers. For this reason, in this research for RIVER FLOW FORECASTING, we have used models of FIS and ANFIS which are based on fuzzy logic. Data of daily flow discharges were provided from Lighvanchay watershed for 6 years. For considering the randomness of data, return points test was used. Then correlogram of data was employed to determine the input optimum models and finally 5 models of discharge forecasting designed based on previous days' discharge. The results showed that ANFIS was more precise and less disperse (RMSE=0.0234) with compare to FIS (RMSE=0.1982). The ANFIS was also more precise in peak discharges simulation than FIS.

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    APA: Copy

    NABIZADEH, M., MOSAEDI, A., HESAM, M., DEHGHANI, A.A., ZAKERINIA, M., & MEFTAH, M.. (2012). RIVER FLOW FORECASTING USING FUZZY INFERENCE SYSTEM (FIS) AND ADAPTIVE NEURO- FUZZY INFERENCE SYSTEM (ANFIS). IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING, 5(17), 7-14. SID. https://sid.ir/paper/134766/en

    Vancouver: Copy

    NABIZADEH M., MOSAEDI A., HESAM M., DEHGHANI A.A., ZAKERINIA M., MEFTAH M.. RIVER FLOW FORECASTING USING FUZZY INFERENCE SYSTEM (FIS) AND ADAPTIVE NEURO- FUZZY INFERENCE SYSTEM (ANFIS). IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING[Internet]. 2012;5(17):7-14. Available from: https://sid.ir/paper/134766/en

    IEEE: Copy

    M. NABIZADEH, A. MOSAEDI, M. HESAM, A.A. DEHGHANI, M. ZAKERINIA, and M. MEFTAH, “RIVER FLOW FORECASTING USING FUZZY INFERENCE SYSTEM (FIS) AND ADAPTIVE NEURO- FUZZY INFERENCE SYSTEM (ANFIS),” IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING, vol. 5, no. 17, pp. 7–14, 2012, [Online]. Available: https://sid.ir/paper/134766/en

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