Cellular automata for simulating complex land use systems using neural networks
Cellular automata for simulating complex land use systems using neural networks
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作者：
L. Xia
本文提出了一种基于神经网络、元胞自动机和GIS相结合的土地利用动态模拟方法。近年来，元胞自动机（CA）越来越多地用于模拟城市增长和土地利用动态。然而，使用CA模型模拟多种土地利用变化是困难的，因为必须使用大量的空间变量和参数。传统CA模型在定义仿真参数值、转换规则和模型结构等方面存在问题。本文设计了一个具有多个输出神经元的三层神经网络，用于计算多种土地利用竞争的转换概率。利用ARC/INFO GRID AML直接在GIS环境下开发了基于神经网络的CA模型。GIS为神经网络的构建提供了数据分析和空间分析功能。从GIS数据库中检索实际数据，方便地对模型进行校准和测试。利用GIS函数进行神经网络计算。神经网络具有多个输出神经元，在每次迭代中生成转换概率。土地利用转换是通过比较转换概率来决定的。该模型通过神经网络的迭代循环来模拟多种土地利用变化。通过神经网络可以从局部相互作用中生成复杂的全局模式。模拟结果不确定，因为使用了随机变量，并且在每个循环结束时动态更新场地属性。该方法克服了现有CA模型在模拟复杂城市系统和多种土地利用变化方面的不足，大大减少了定义参数值、转换规则和模型结构的繁琐工作。该模型已成功应用于珠江三角洲土地利用动态模拟。
This paper presents a new method to simulate the dynamics of multiple land uses based on the integration of neural networks,cellular automata and GIS. Recently, cellular automata (CA) have been increasingly used to simulate urban growth and land use dynamics. However, simulation of multiple land use changes using CA models is difficult because numerous spatial variables and parameters have to be utilized. Conventional CA models have problems in defining simulation parameter values, transition rules and model structures. In this paper, a three-layer neural network with multiple output neurons is designed to calculate conversion probabilities for competing multiple land uses. The neural-network-based CA model is directly developed in a GIS environment by using ARC/INFO GRID AML. The GIS provides both data and spatial analysis functions for constructing the neural network. Real data are conveniently retrieved from the GIS database for calibrating and testing the model. The GIS functions are also used for the neural network calculations. The neural network has multiple output neurons to generate conversion probabilities at each iteration. Land use conversion is decided by comparing the conversion probabilities. The model is carried out by iterative looping the neural network for simulating multiple land use changes. Complex global patterns can be generated from local interactions through the neural network. The simulation results are not deterministic because a stochastic variable is used and site attributes are dynamically updated at the end of each loop. The proposed method can overcome some of the shortcomings of the currently used CA models in simulating complex urban systems and multiple land use changes by significantly reducing the tedious work in defining parameter values, transition rules and model structures. The model has been successfully applied to the simulation of land use dynamics in the Pearl River Delta.