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Cites:

1

Information Journal Paper

Title

SIMULATION OF MALAYER PLAIN GROUNDWATER LEVEL BASED ON WEATHER DATA USING ARTIFICIAL NEURAL NETWORK

Pages

  17-28

Abstract

 Prediction of groundwater level is necessary for supply management and utilization of water. Groundwater Fluctuations is influenced by many variables. One of the most appropriate methods, the study of groundwater, is using computer models. Thus, understanding of ground water variation mechanism and its prediction is necessary for awareness of available groundwater.In this regard, ARTIFICIAL NEURAL NETWORK models, due to lack of understanding of the physical nature of the problem in modeling nonlinear systems are capable. Studies in Iran show that, in spite of the importance of groundwater level prediction and intelligent neural network capabilities, few studies regarding water table SIMULATIONs using ARTIFICIAL NEURAL NETWORKs has been done so far.According to the necessities expressed purpose of this study is establish the relation between of groundwater level fluctuations associated with effective ARTIFICIAL NEURAL NETWORK, in order to simulate and quantify associated water table fluctuations as an independent variable with various meteorological factors and hydro geology of MALAYER PLAIN.

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  • Cite

    APA: Copy

    ZARE ABYANEH, H., BAYAT VARKESHI, M., MAROFI, S., & ILDROMI, A.R.. (2012). SIMULATION OF MALAYER PLAIN GROUNDWATER LEVEL BASED ON WEATHER DATA USING ARTIFICIAL NEURAL NETWORK. PHYSICAL GEOGRAPHY RESEARCH QUARTERLY, -(78), 17-28. SID. https://sid.ir/paper/138876/en

    Vancouver: Copy

    ZARE ABYANEH H., BAYAT VARKESHI M., MAROFI S., ILDROMI A.R.. SIMULATION OF MALAYER PLAIN GROUNDWATER LEVEL BASED ON WEATHER DATA USING ARTIFICIAL NEURAL NETWORK. PHYSICAL GEOGRAPHY RESEARCH QUARTERLY[Internet]. 2012;-(78):17-28. Available from: https://sid.ir/paper/138876/en

    IEEE: Copy

    H. ZARE ABYANEH, M. BAYAT VARKESHI, S. MAROFI, and A.R. ILDROMI, “SIMULATION OF MALAYER PLAIN GROUNDWATER LEVEL BASED ON WEATHER DATA USING ARTIFICIAL NEURAL NETWORK,” PHYSICAL GEOGRAPHY RESEARCH QUARTERLY, vol. -, no. 78, pp. 17–28, 2012, [Online]. Available: https://sid.ir/paper/138876/en

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