基于WCA2D与SWMM模型的城市暴雨洪涝快速模拟

为探讨计算高效的元胞自动机模型(WCA2D)与传统一维管网模型耦合的机制以及计算效果,尝试将WCA2D与SWMM模型耦合(SWMM/WCA2D),以广州市长湴片区为例探究一种暴雨洪涝快速二维模拟技术,对比实测积水数据以及SWMM/LISFLOOD-FP模拟,结果表明:SWMM/WCA2D模拟结果与"20180607"实测积水数据相近,表明模型精度良好;根据多指标评估结果,综合考虑主干渠道淤积以及建筑物阻挡情景的RTPR、RPPV、F1值分别达到0.8、0.6、0.7,模拟精度最高,最能反映区域实际情况;通过与SWMM耦合,WCA2D和LISFLOOD-FP的模拟结果差异小(最大水深差值基本低于0.1 m)、相关性强(相关系数基本超过0.7),但前者计算效率是后者的3~5倍,表明WCA2D能够耦合SWMM且计算效率更高,为复杂城市化地区暴雨洪涝快速模拟提供了一种新方法。     

基于空间的生态足迹与生态承载力预测模型——以甘肃省河西走廊地区为例

介绍了测度可持续发展的生态足迹分析的计算方法,在此基础上提出了生态足迹和生态承载力的预测模型:在生态足迹的预测方面采用消费预测模型和人口模型相结合的方法,在生态承载力的预测方面采用地理元胞自动机和地理信息系统技术相结合的方法。以甘肃省河西走廊地区为例,采用以上模型方法计算了1995、2000年的生态足迹和生态承载力,预测了2005年的生态足迹和生态承载力,结果表明:河西走廊地区的生态足迹在逐年上升,生态承载力前5年上升后5年下降。采用该模型方法计算与预测生态足迹和生态承载力,具有快速、准确、可操作性强的特点,但预测具有一定的局限性。     

Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework

Cellular automata (CA) and artificial neural networks (ANNs) have been used by researchers over the last three decades to simulate land-use change (LUC). While conventional CA and ANN models assign a cell to only one land-use class, in reality, a cell may belong to several land-use classes simultaneously. The recently developed multi-label (ML) concept overcomes this limitation in land change science. Although the ML concept is a new paradigm with nonexclusive classes and has shown considerable merit in several applications, few studies in land change science have applied it. In addition, determining transition rules in conventional CA is difficult when the number of drivers is large. Since CA has been shown as a potential model to consider neighborhood effects and ANN has been shown effective in determining CA transition rules, we integrated both CA with an ANN model to overcome limitations of each tool. In this study, we specifically extended the ANN-based Land Transformation Model (LTM) with both a CA-based model and the ML concept to create an integrated ML-CA-LTM modeling framework. We also compared, using standard evaluation measures, differences between the proposed integrated model with a conventional CA-based LTM model (called the ml-CA-LTM). Parameterization was made using a learning and testing procedure common in machine learning. Results showed that the modified LUC model, ML-CA-LTM, produced consistently better goodness of fit calibration values compared to the ml-CA-LTM. The outcome of this modified model can be used by managers and decision makers for improved urban planning.     

Modeling different urban growth patterns based on the evolution of urban form: A case study from Huangpi,Central China

Spatial patterns of urban expansion mainly include infilling, edge expansion, and outlying growth patterns. The cellular automata (CA) model, is an important spatio-temporal dynamic model and effectively simulates infilling and edge-expansion urban expansion. but is evidently lacking in outlying scenarios. In addition, CA cannot explain the causes and processes of urban land expansion. Given these limitations, we proposed a novel urban expansion model called simulation model of different urban growth pattern (SMDUGP), which can work well in both adjacent (i.e., infilling and edge expansion) and outlying growth patterns. SMDUGP has two main components. First, we divided the non-urban region into two categories, namely, candidate region for adjacent pattern urban growth (CRFAP) and candidate region for outlying pattern urban growth (CRFOP). Second, different methods were utilized to simulate urban expansion in the different categories. In CRFAP, a CA model based on the potential of urban growth was proposed to simulate urban growth in relatively randomly selected urban growth regions based on the discrete selection model and Monte Carlo method as the expansion area was implemented in CRFOP. Huangpi, a suburban area in Wuhan, is utilized as the case study area to simulate the spatial and temporal dynamics of urban growth from 2004 to 2024. SMDUGP can effectively simulate outlying urban growth with a highly improved simulation precision compared with the traditional CA model and can explain the causes and processes of urban land expansion.     

地理元胞自动机模型研究进展

元胞自动机(Cellular Automata,简称CA)是一种基于微观个体的相互作用空间离散动态模型,其强大的计算功能、固有的平行计算能力、高度动态及空间概念等特征,使它在模拟空间复杂系统的时空动态演变研究具有较强的优势。文章回顾了元胞自动机的发展历程,阐述了CA在地理学中的主要应用领域和研究进展,在此基础上,以现实世界地理实体及现代城市扩张特征为视角,分析目前CA研究所面临的问题,并对其未来的研究趋势进行了初步探讨,认为以下3个方面将是未来CA研究的热点：① 利用不规则元胞及可控邻域的CA模型,对不同规则或不同邻域地理实体的模拟研究;② 采用三维元胞自动机对现代城市扩张进行立体化模拟,以克服二维CA模型的缺陷;③ 将矢量元胞自动机模型应用于地理实体的模拟研究,进一步提高模拟精度。     

城市与区域定量研究进展

本文系统地梳理了城市与区域的定量研究方法,主要包括数理模型和模拟方法、单个城市增长和动力系统研究、城市系统动力学模型及其发展过程模拟研究、空间一般均衡模型研究以及多模型复合/集成系统研究等。主要结论为：1950年以前的城市与区域研究主要采取时间序列预测法,依靠历史资料的时间数列进行趋势外推研究;1960-1970年代主要采用数理统计分析方法,应用人口统计学模型和线性回归方法进行研究;1970-1980年代,随着系统科学的发展,采用系统分析方法发展了诸如交通、人口、土地等专业模型,展开对城市与区域问题的深入分析;1990-2000年代,研究数据的获取手段得到极大的改善,利用数据模型、地计算分析、模拟预测等分析手段使得研究的深度和广度均有了长足进步。近年来,由于空间数据挖掘的突破,大数据、云计算和巨型模型系统开始进入城市与区域研究领域,多模型复合/集成系统研究成为主要研究方向。     

A bat-inspired approach to define transition rules for a cellular automaton model used to simulate urban expansion

A new metaheuristic approach is presented to discover transition rules for a cellular automaton (CA) model using a novel bat movement algorithm (BA). CA is capable of simulating the evolution of complex geographical phenomena, and transition rules lie at the core of these models. An intelligence algorithm based on the echolocation behavior of bats is used to discover explicit transition rules for use in simulating urban expansion. CA transition rules are formed by links between attribute constraint items and classification items. The transition rules are derived using the BA to optimize the lower and upper threshold values for each attribute. The BA-CA model is then constructed for the simulation of urban expansion observed for Nanjing City, China. The total accuracy of newly formulated BA-CA model for this application is 86.9%, and the kappa coefficient is 0.736, which strongly suggest that the interactions of bats are effective in capturing the relationships between spatial variables and urban dynamics. It is further demonstrated that this bat-inspired BA-CA model performs better than the null model, the particle swarm optimization-based CA model (PSO-CA), and the ant colony optimization-based CA model (ACO-CA) using the same dataset. The model validation and comparison illustrate the novel capability of BA for discovering transition rules of CA during the simulation of urban expansion and potentially for other geographic phenomena.     

Gradually morphing a thematic map series based on cellular automata

Maps are often animated to help users make comparisons and comprehend trends. However, large and complex differences between sequential maps can inhibit users from doing so. This paper proposes a morphing technique to highlight trends without manual intervention. Changes between sequential maps are considered as the diffusion processes of expanding classes, with these processes simulated by cellular automata. A skeleton extraction technique is introduced to handle special cases. Experimental results demonstrate that the proposed morphing technique can reveal obvious trends between dramatically changed maps. The potential application of the proposed morphing technique in sequential spatial data (e.g. remote-sensing images) is discussed.     

城市群-开发区-产业集群互动研究的理论思考

以相互作用理论为指导,系统解析了城市群-开发区-产业集群互动研究的总体思路、研究内容与研究方法。基于理论分析,提出城市群-开发区-产业集群是相互耦合的复杂网络系统,三者的互动研究应遵循互动影响因素分析、互动方式识别、互动机制揭示、互动强度与协调度测度、互动模式探讨和互动路径设计的分析框架,并采用多学科融贯的综合研究、大数据分析、GIS空间分析、多智能体和元胞自动机建模等方法进行研究。     

基于QGIS的黄河三角洲岸线演变预测系统设计与实现

利用元胞自动机模型和蒙特卡罗方法,确定元胞转换规则,构建了一套基于黄河三角洲地区的岸线冲淤演变预测模型,并结合泥沙遥感反演结果和历年遥感图像数据分析近几年岸线演变情况,把开源QGIS作为二次开发平台,构建了黄河三角洲岸线演变预测系统。该系统具有数据管理、地图管理、海岸线预测、数据查询和用户登录等五大功能模块,实现了黄河三角洲海岸线的可视化管理及准确预测。