Simulation of development alternatives using neural networks, cellular automata, and GIS for urban planning
Simulation of development alternatives using neural networks, cellular automata, and GIS for urban planning
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DOI：
10.14358/pers.69.9.1043
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发表时间：
2003-09
影响因子：
1.3
通讯作者：
A. Yeh;Xia Li
中科院分区：
文献类型：
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作者：
A. Yeh;Xia Li
本研究整合类神经网路与细胞自动机（CA）来模拟规划用途的发展方案.现有的大部分CA只是专注于模拟现实的城市动态。本文论证了将规划目标纳入元胞自动机中可以模拟开发方案，但根据规划者和决策者的规划目标确定合适的参数值是模拟开发方案的关键。训练神经网络可以自动产生城市模拟的参数值。GIS和遥感为模型的标定提供了训练数据。然而，如果使用原始训练数据来校准模型，则模拟可以继承过去的土地利用问题。对原始数据进行评估和修改，使模型能够记住过去土地开发中的“失败”。因此，通过适当修改训练数据集，可以将规划目标嵌入模型中。训练是鲁棒的，因为它是基于定义良好的反向传播算法。并以东莞市为例进行了实证分析。
This study integrates neural networks and cellular automata (CA )t osimulate development alternatives for planning purposes. Most of the existing CA just focus on simulating realistic urban dynamics. This paper demonstrates that development alternatives can be simulated by incorporating planning objectives in CA .I ti simportant to define appropriate parameter values for simulating development alternatives according to the planning objectives of planners and decision makers. Training neural networks can automatically yield the parameter values for urban simulation. GIS and remote sensing provide the training data for calibrating the model. However, the simulation can inherit past land-use problems if the original training data are used to calibrate the model. The original data should be assessed and modified so that the model can remember the past “failure” in land development. Planning objectives can thus be embedded in the model by properly modifying the training data sets. The training is robust because it is based on the welldefined back-propagation algorithm. Experiments were carried out by using the city of Dongguan, China as an example to test the model.