基于神经网络的元胞自动机及模拟复杂土地利用系统
收稿日期: 2004-05-25
修回日期: 2004-09-01
网络出版日期: 2005-02-15
基金资助
国家自然科学基金资助项目(40471105);高等学校博士学科点专项科研基金资助(20040558023)
Cellular automata for simulating complex land use systems using neural networks
Received date: 2004-05-25
Revised date: 2004-09-01
Online published: 2005-02-15
黎夏, 叶嘉安 . 基于神经网络的元胞自动机及模拟复杂土地利用系统[J]. 地理研究, 2005 , 24(1) : 19 -27 . DOI: 10.11821/yj2005010003
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.
Key words： neural networks; cellular automata; remote sensing; land use; GIS
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