Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace

Research Article

Prediction model of spatial distribution pattern of building based on Neural Network

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  • @INPROCEEDINGS{10.4108/eai.27-8-2020.2294610,
        author={Jia Jie  YU and lin  LIU},
        title={Prediction model of spatial distribution pattern of building based on Neural Network},
        proceedings={Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace},
        publisher={EAI},
        proceedings_a={MOBIMEDIA},
        year={2020},
        month={11},
        keywords={neural network;architecture;spatial distribution pattern;change;forecast;},
        doi={10.4108/eai.27-8-2020.2294610}
    }
    
  • Jia Jie YU
    lin LIU
    Year: 2020
    Prediction model of spatial distribution pattern of building based on Neural Network
    MOBIMEDIA
    EAI
    DOI: 10.4108/eai.27-8-2020.2294610
Jia Jie YU1, lin LIU,*
  • 1: Department of architecture and art,ChongQing JianZhu College
*Contact email: yj159951000@163.com

Abstract

The traditional prediction model of building spatial distribution pattern change has the problem of poor prediction accuracy, so a prediction model of building spatial distribution pattern change based on neural network is designed. Based on the analysis of the spatial characteristics of traditional villages in Southeast Chongqing, this paper uses the neural network method to predict the change of the spatial distribution pattern of buildings, so as to complete the construction of the prediction model of the change of the spatial distribution pattern of buildings, and puts forward the planning and design strategies of traditional villages in Southeast Chongqing for the effective planning of traditional villages in Southeast Chongqing. The experimental results show that the prediction model based on neural network is more accurate than the traditional model, and can meet the needs of the prediction of the change of the spatial distribution pattern of buildings.