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首页> 外文期刊>Journal of Geographic Information System >Comparison of Urban Growth Modeling Using Deep Belief and Neural Network Based Cellular Automata Model—A Case Study of Chennai Metropolitan Area, Tamil Nadu, India
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Comparison of Urban Growth Modeling Using Deep Belief and Neural Network Based Cellular Automata Model—A Case Study of Chennai Metropolitan Area, Tamil Nadu, India

机译：基于深层信念和基于神经网络的元胞自动机模型的城市增长模型的比较-以印度泰米尔纳德邦金奈大都市区为例

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摘要

Urban Growth Models (UGMs) are very essential for a sustainable development of a city as they predict the future urbanization based on the present scenario. Neural Network based Cellular Automata models have proved to predict the urban growth more close to reality. Recently, deep learning based techniques are being used for the prediction of urban growth. In this current study, urban growth of Chennai Metropolitan Area (CMA) of 2017 was predicted using Neural Network based Cellular Automata (NN-CA) model and Deep belief based Cellular Automata (DB-CA) model using 2010 and 2013 urban maps. Since the study area experienced congested type of urban growth, “Existing Built-Up” of 2013 alone was used as the agent of urbanization to predict urban growth in 2017. Upon validating, DB-CA model proved to be the better model, as it predicted 524.14 km~(2) of the study area as urban with higher accuracy (kappa co-efficient: 0.73) when compared to NN-CA model which predicted only 502.42 km~(2) as urban (kappa co-efficient: 0.71), while the observed urban cover of CMA in 2017 was 572.11 km~(2). This study also aimed at analyzing the effects of different types of neighbourhood configurations (Rectangular: 3 × 3, 5 × 5, 7 × 7 and Circular: 3 × 3) on the prediction output based on DB-CA model. To understand the direction and type of the urban growth, the study area was divided into five distance based zones with the State Secretariat as the center and entropy values were calculated for the zones. Results reveal that Chennai Corporation and its periphery experience congested urbanization whereas areas away from the Corporation boundary follow dispersed type of urban growth in 2017.
机译：城市增长模型（UGM）对于城市的可持续发展非常重要，因为它们基于当前情景预测了未来的城市化进程。实践证明，基于神经网络的元胞自动机模型可以预测城市的增长，使其更加接近现实。最近，基于深度学习的技术正在用于预测城市增长。在本研究中，使用2010年和2013年城市地图的基于神经网络的元胞自动机（NN-CA）模型和基于深层信念的元胞自动机（DB-CA）模型来预测2017年钦奈都市区的城市增长。由于研究区域经历了城市增长的拥堵类型，仅2013年的“现有建筑”就被用作城市化的代理来预测2017年的城市增长。经过验证，DB-CA模型被证明是更好的模型，因为它与仅预测502.42 km〜（2）的城市（kappa系数：0.71）的NN-CA模型相比，将研究区域预测为524.14 km〜（2）的城市具有较高的准确性（kappa系数：0.73）。 ，而2017年CMA的城市覆盖观测值为572.11 km〜（2）。本研究还旨在分析不同类型的邻域配置（矩形：3×3、5×5、7×7和圆形：3×3）对基于DB-CA模型的预测输出的影响。为了了解城市增长的方向和类型，将研究区域分为五个基于距离的区域，以国家秘书处为中心，并计算区域的熵值。结果显示，钦奈公司及其周边地区经历了城市化拥挤，而2017年，远离公司边界的地区遵循了分散的城市增长类型。

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