城镇化进程中基于蚁群行为规则的滇池流域不透水表面扩张智能体建模与模拟

不透水表面（IS）的快速增加是导致滇池流域城市非点源污染不断加剧的主要原因，而不透水表面覆盖率（ISC）是城镇化发展水平与非点源污染风险预警的双重评价指标，有必要研究流域不透水表面扩张的情景模拟模型，模拟、分析和预测流域城镇化空间格局演变的规律，进而解释流域不透水表面扩张的动力作用机制，从而促进流域城镇化与水环境保护相协调的可持续性发展。当前，基于智能体建模（ABM）的不透水表面变化模拟是地理模拟领域的研究趋势，而智能体行为建模及优化是该领域面临的关键性挑战之一。为此，本研究以蚁群行为规则在智能体建模中的优化为目标，研究蚁群算法与智能体行为建模的耦合机制及概念模型，设计微观与宏观相结合的智能体启发式行为与决策规则、参数敏感性检验与模型验证等方法，最终构建滇池流域不透水表面扩张的动态模拟模型，为流域城镇化发展的良性用地模式提供科学理论依据和信息化决策工具，有望丰富城镇化用地扩张的智能体建模优化理论与方法

The continuous aggravating of urban Non-point source pollution is due to the rapid increasing of impervious surface(IS) area for Dian-chi Lake watershed， and impervious surface coverage(ISC) is a double assessment indicator of urbanlization level and risk pre-warning of Non-point source pollution. Thereby, it is necessary to study the scenario simulation model of IS expansion in order to promote the harmonious development between urbanization and water environment conservation in the watershed by simulating, analyzing and forecasting the rules and tend of urban spatial pattern evolution, as well as explaining drvien mechanism of IS expansion in urbanization. Presently, it is a major research trend how to simulate IS expasion used Agent-based modeling(ABM) in Geosimulation research area, and behavior modeling and it's optimization based on ABM also is one of the key challenges in this area. Therefore, in this research proposal, taking the optimization of ant behavior rules used in ABM as a goal, we study the coupling mechanism between ant colony optimization and agent behavior modeling, as well as their concept model, and design the heuristic rules of agent behavior and decision-making on both microcosmic and macroscopic point, as well as the methodologies of model validation and verification for parameters sensitivity. And finally we will develop a simulation model of IS dynamic change. Through those researches above all, it is hopeful to provide the scientific theoretical supports and information-based decision-making tools for benign land use mode in urbanization of watershed, as well as to enrich the theories and methods system of optimized Agent-based modeling.