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Information Journal Paper

Title

FORECASTING ISFAHAN PRECIPITATION WITH ARTIFICIAL NEURAL NETWORKS

Pages

  47-63

Abstract

PRECIPITATION is the most important climate and meteorology factor. In this study monthly PRECIPITATION data of ISFAHAN synoptic station during 1951-2009 has been used. Because of none linearly of PRECIPITATION during time, The ARTIFICIAL NEURAL NETWORKs have been used.495 (70%) of data has been used as training and 231 data about (30%) as testing and validation. The Results of this study after network testing with different hidden layer and training coefficient indicated that using of ARTIFICIAL NEURAL NETWORK with 2 hidden layer Perceptron, 0/4 training coefficient has presentation comparatively a better model. So after testing again network and training with different hidden layer and training coefficient in combination with GENETIC ALGORITHM indicated that combination of network with mentioned characters with GENETIC ALGORITHM decrease the error and increase speed of calculation and present a better model. The random and arranged PRECIPITATION data didn’t have any effect on the results. It is necessary to be mentioned that data combination neural networks with algorithm genetic result in increased accuracy and better fitting the model.

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    Cite

    APA: Copy

    HALABIAN, AMIR HOSSIEN, & DARAND, MOHAMMAD. (2012). FORECASTING ISFAHAN PRECIPITATION WITH ARTIFICIAL NEURAL NETWORKS. JOURNAL OF GEOGRAPHICAL SCIENCES, 12(26), 47-63. SID. https://sid.ir/paper/102413/en

    Vancouver: Copy

    HALABIAN AMIR HOSSIEN, DARAND MOHAMMAD. FORECASTING ISFAHAN PRECIPITATION WITH ARTIFICIAL NEURAL NETWORKS. JOURNAL OF GEOGRAPHICAL SCIENCES[Internet]. 2012;12(26):47-63. Available from: https://sid.ir/paper/102413/en

    IEEE: Copy

    AMIR HOSSIEN HALABIAN, and MOHAMMAD DARAND, “FORECASTING ISFAHAN PRECIPITATION WITH ARTIFICIAL NEURAL NETWORKS,” JOURNAL OF GEOGRAPHICAL SCIENCES, vol. 12, no. 26, pp. 47–63, 2012, [Online]. Available: https://sid.ir/paper/102413/en

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