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Information Journal Paper

Title

Evaluating the Performance of Time-Series, Neural Network and Neuro-Fuzzy Models in Prediction of Meteorological Drought (Case study: Semnan Synoptic Station)

Pages

  1-18

Keywords

Artificial neural network (ANN)Q2
Adaptive neurofuzzy inference system (ANFIS)Q1

Abstract

 Drought phenomenon is one of the natural and creeping disasters, which occurs in almost every climate and its properties vary spatially. A considerable number of scientific research has been done on drought in Iran and throughout the world. These studies have examined various aspects of drought. Through such research and knowledge effective and efficient solutions could be found to deal with good management of drought. Since Iran is located in an arid region of the world, nowhere in the country is immune from this phenomenon. This research has attempted to present appropriate models to predict drought for the city of Semnan, Iran.

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    APA: Copy

    SADEGHIAN, M., KARAMI, H., & MOUSAVI, S.F.. (2020). Evaluating the Performance of Time-Series, Neural Network and Neuro-Fuzzy Models in Prediction of Meteorological Drought (Case study: Semnan Synoptic Station). IRRIGATION SCIENCES AND ENGINEERING (JISE) (SCIENTIFIC JOURNAL OF AGRICULTURE), 43(2 ), 1-18. SID. https://sid.ir/paper/377697/en

    Vancouver: Copy

    SADEGHIAN M., KARAMI H., MOUSAVI S.F.. Evaluating the Performance of Time-Series, Neural Network and Neuro-Fuzzy Models in Prediction of Meteorological Drought (Case study: Semnan Synoptic Station). IRRIGATION SCIENCES AND ENGINEERING (JISE) (SCIENTIFIC JOURNAL OF AGRICULTURE)[Internet]. 2020;43(2 ):1-18. Available from: https://sid.ir/paper/377697/en

    IEEE: Copy

    M. SADEGHIAN, H. KARAMI, and S.F. MOUSAVI, “Evaluating the Performance of Time-Series, Neural Network and Neuro-Fuzzy Models in Prediction of Meteorological Drought (Case study: Semnan Synoptic Station),” IRRIGATION SCIENCES AND ENGINEERING (JISE) (SCIENTIFIC JOURNAL OF AGRICULTURE), vol. 43, no. 2 , pp. 1–18, 2020, [Online]. Available: https://sid.ir/paper/377697/en

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