作 者 ：郁亚娟,郭怀成,刘永,黄凯,王真
期 刊 ：生态学报 2007年 27卷 6期 页码：2603~2614
Keywords:dynamic modeling, urban ecosystem, uncertainty,
摘 要 ：从系统分析出发，对城市生态系统的动力学演化模型的发展历程、建模的方法和步骤过程、软件开发方法和目前的模型软件等进行了总结。归纳了城市生态系统的动力学演化建模的方法，主要包括模型定义、模拟、实现、验证、分析和应用等六大步骤。目前国内外用于城市生态系统动力学演化模型的主要方法有：基于数理模型的方法、生态控制论和灵敏度模型、系统动力学模型、多目标规划法等。已经开发的用于城市生态系统的动力学模拟的软件可以划分为两类：基于土地利用和交通规划的专业模型和基于系统动力学和灵敏度模型的一般软件。总结了常用的城市演化模型软件，讨论了模型的研究对象和应用范围。分析了城市生态系统的动力学演化模型建模的不确定性的来源，并指出：向宏观和微观两极化发展是城市生态系统动力学演化模型的发展趋势之一，而与人工智能和地理信息系统等新方法的集成是发展的另一趋势。城市生态系统动力学演化模型的开发前景在于对不确定性问题的定性、定量分析，而多模型的耦合和集成是发展的必然趋势。
Abstract:This paper presents a comprehensive literature review on the progress in Dynamic Urban Ecosystem Modeling (DUEM), summarizing various perspectives such as the history, method, procedure, and software development and availability that are pertinent to the urban ecosystem modeling area. DUEM represents a multi-disciplinary research area, which covers many related scientific disciplines including landscape ecology, urban demography, sociology, urban planning, environmental planning, environmental economics, disaster prevention science, urban hygiene, and public health. The history of the DUEM area can be traced back to the 19th century when researchers started to study the process of urban expansion and evolution. Since then, researchers throughout the world have developed a wide range of modeling approaches to help understand the dynamics and evolution of urban ecosystems. Before 1960s, the ecological and environmental aspects of urban development have rarely been considered in an urban system modeling study. This trend started to change in 1960s, which was manifested by the fashion of developing comprehensive urban ecosystem models that simulate not only the social-economic but also the ecological and environmental components in urban systems.
Based on the difference in mathematical formulation and system representation, all the existing urban ecosystem models can be roughly classified into four broad categories: (1) mathematical mechanistic models; (2) eco-cybernetics based sensitivity models; (3) system dynamics model; and (4) multi-objective models. In addition to these four branches of methods, other types of modeling approaches such as the ecological footprint method, the scenario analysis method, and the entropy-based approach have also attracted wide attentions from researchers. Despite of their difference in appearance, all these modeling methods follow a same general procedure, which involves six steps including model definition, model formulation, computer realization, calibration/validation, model performance evaluation, and model application.
Since the day researchers began to research the possibility of using mathematical models to study urban systems, numerous modeling systems have been developed. Some of these models are developed through direct computer programming with general computer languages, and the others are developed using popular simulation and optimization software packages such as the VENSIM, STELLA, DYNAMO, and Matlab’s Simulink toolbox. Table 1 in the present paper lists a number of the most widely applied urban ecosystem models along with information regarding their developers and applicability.
This paper further addresses the issue of uncertainty in urban system modeling, indicating that an urban system modeling is always subjected to uncertainty originated from the entire model development and application process. Due to the significant implication of uncertainty, it is proposed that special efforts need to be dedicated to improve the capability of handling model uncertainty in a DUEM study. Finally, this paper summarizes potential research directions for the DUEM area, suggesting that technologies and methods need to be extended to both macro and micro scales to achieve further advancement in this area. In addition, to develop hybrid approach through integrating advanced artificial intelligence technologies and geographical information systems might offer another promising way for model improvement.