USING NEURAL NETWORK FOR URBAN GROWTH MODELING (CASE STUDY: GORGAN CITY)

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KAMYAB H.R.; SALMAN MAHINY A.; HOSEINI S.M.; GHOLAMALIFARD M.

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Journal: Human Geography Research Year:2011 | Volume:43 | Issue:76 Page(s): 99-113

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Information Journal Paper

Title

USING NEURAL NETWORK FOR URBAN GROWTH MODELING (CASE STUDY: GORGAN CITY)

Author(s)

KAMYAB H.R. | SALMAN MAHINY A. | HOSEINI S.M. | GHOLAMALIFARD M. | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKQ2

URBAN GROWTH MODELINGQ2

LAND TRANSFORMATION MODELQ2

ROCQ2

GORGAN CITYQ2

Abstract

Introduction: ARTIFICIAL NEURAL NETWORK (ANN) models are knowledge-based models and fit within the regression type models of land use changes. ANNs are powerful tools that use a machine learning approach to quantify and model complex behavior and patterns. ANNs were developed to model the brain's interconnected system of neurons so that computers could be made to imitate the brain’s ability to sort patterns and learn from trial and error, thus observing relationships in data.

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APA: Copy

KAMYAB, H.R., SALMAN MAHINY, A., HOSEINI, S.M., & GHOLAMALIFARD, M.. (2011). USING NEURAL NETWORK FOR URBAN GROWTH MODELING (CASE STUDY: GORGAN CITY). HUMAN GEOGRAPHY RESEARCH QUARTERLY, 43(76), 99-113. SID. https://sid.ir/paper/138929/en

Vancouver: Copy

KAMYAB H.R., SALMAN MAHINY A., HOSEINI S.M., GHOLAMALIFARD M.. USING NEURAL NETWORK FOR URBAN GROWTH MODELING (CASE STUDY: GORGAN CITY). HUMAN GEOGRAPHY RESEARCH QUARTERLY[Internet]. 2011;43(76):99-113. Available from: https://sid.ir/paper/138929/en

IEEE: Copy

H.R. KAMYAB, A. SALMAN MAHINY, S.M. HOSEINI, and M. GHOLAMALIFARD, “USING NEURAL NETWORK FOR URBAN GROWTH MODELING (CASE STUDY: GORGAN CITY),” HUMAN GEOGRAPHY RESEARCH QUARTERLY, vol. 43, no. 76, pp. 99–113, 2011, [Online]. Available: https://sid.ir/paper/138929/en

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