Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the Athens metropolitan area
Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the Athens metropolitan area
复制标题
DOI：
10.1016/j.cities.2012.03.006
复制
发表时间：
2013-02-01
期刊：
影响因子：
6.7
通讯作者：
Photis, Yorgos N.
中科院分区：
文献类型：
--
作者：
Grekousis, George;Manetos, Panos;Photis, Yorgos N.
本文提出了一种人工智能方法与地理信息系统（GIS）集成的城市演变建模。模糊逻辑和神经网络被用来提供一个综合的时空方法的分析，预测和解释的城市增长。拟议的城市模式考虑到人口和建筑物使用模式随时间的变化。一个地理信息系统用于处理空间和时间数据，进行应急分析和映射的结果。具有相似特征的空间实体通过使用模糊c-均值算法分组在一起。每一组代表城市增长和发展的特定水平。一个两层前馈多层感知器人工神经网络，然后用于预测城市增长。该模型应用于希腊阿提卡地区，描绘了雅典大都市区的当前和未来的演变趋势，这是说明了城市增长动力学的地图。拟议的方法旨在协助规划者和决策者深入了解从农村向城市的过渡。(C)2012爱思唯尔有限公司保留所有权利。
This paper presents an artificial intelligence approach integrated with geographical information systems (GISs) for modeling urban evolution. Fuzzy logic and neural networks are used to provide a synthetic spatiotemporal methodology for the analysis, prediction and interpretation of urban growth. The proposed urban model takes into account the changes over time in population and building use patterns. A GIS is used for handling the spatial and temporal data, performing contingency analysis and mapping the results. Spatial entities with similar characteristics are grouped together in clusters by the use of a fuzzy c-means algorithm. Each cluster represents a specific level of urban growth and development. A two-layer feed-forward multilayer perceptron artificial neural network is then used to predict urban growth. The model, applied to the prefecture of Attica, Greece, delineates the current and future evolution trends of the Athens metropolitan area, which are illustrated by maps of the urban growth dynamics. The proposed methodology aims to assist planners and decision makers in gaining insight into the transition from rural to urban. (C) 2012 Elsevier Ltd. All rights reserved.