地理科学进展 ›› 2005, Vol. 24 ›› Issue (1): 105-115.doi: 10.11820/dlkxjz.2005.01.012

• 城市地理与GIS应用 • 上一篇    下一篇

元胞模型在地貌演化模拟中的应用浅析

黄 翀, 刘高焕   

  1. 中国科学院地理科学与资源研究所, 资源与环境信息系统国家重点实验室,北京100101
  • 收稿日期:2004-11-01 修回日期:2004-12-01 出版日期:2005-01-25 发布日期:2005-01-25
  • 作者简介:黄翀(1975-), 男, 中国科学院资源与环境信息系统国家重点实验室博士生, 主要从事GIS与遥感应用、元胞自动机及定量地貌研究. E-mail: huangch@lreis.cn
  • 基金资助:

    国家自然科学基金资助项目(40371093)。

A Review of the Application of Cellular Models in Landscape Evolution Modeling

HUANG Chong, LIU Gaohuan   

  1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China
  • Received:2004-11-01 Revised:2004-12-01 Online:2005-01-25 Published:2005-01-25

摘要:

元胞自动机是一个时空离散的动力学模型,是复杂系统的研究方法之一。从80年代后期开始,在许多领域都得到广泛的应用与发展。地貌是一个非线性动态复杂系统,元胞自动机模型(或更广意义上的元胞模型)为研究复杂地貌系统的动态演化提供了新的方法论工具。本文根据近些年国内外该领域的研究成果,对元胞模型在地貌演化模拟中的应用及进展进行了探讨,并分析了元胞模型方法在地貌演化模拟中的优势和不足。

关键词: 地貌演化, 模拟, 突现, 元胞模型, 元胞自动机

Abstract:

Cellular automata are discrete and dynamical systems that are divided up into small cells with each cell taking a certain state. The basic idea of cellular automata is to reduce a complex system of complex rules into something simpler. The theory of cellular automata was originally conceived by Ulam and Von Neumann in the 1940s to provide a model for investigating the behavior of complex and extended systems. In recent years cellular automata have been increasing in popularity in the study of modeling real phenomena occurring in biology, chemistry, ecology, economy, geology, mechanical engineering, medicine, physics, sociology, public traffic, etc. The extended cellular automata or cellular models have many advantages in landscape evolution modeling. First, cellular models include both spatial and temporal contents which are important factors in landscape evolution. Second, the rules and computation in cellular models are simpler and not so many parameters are required. Third, through the local interactions among cells, cellular models can exhibit a lot of complex phenomena such as emergence, chaos, reproduction, et al. Some disadvantages are also reviewed, which include the uncertainty of influence of cell size and the difficulty in result validation. Despite the deficiencies, cellular models exhibit a good prospect in landscape evolution modeling.

Key words: cellular automata, cellular models, emergence, landscape evolution, modeling