基于改进粒子群算法和元胞自动机的城市扩张模拟——以南京为例

doi:10.11870/cjlyzyyhj201702004

长江流域资源与环境 >> 2017, Vol. 26 >> Issue (02): 190-197.doi: 10.11870/cjlyzyyhj201702004

基于改进粒子群算法和元胞自动机的城市扩张模拟——以南京为例

李沁^1,2, 沈明^1,2, 高永年¹, 张志飞³

1. 中国科学院南京地理与湖泊研究所流域地理学重点实验室, 江苏南京 210008;
2. 中国科学院大学, 北京 100049;
3. 江苏省土地勘测规划院, 江苏南京 210024

收稿日期:2016-05-23 修回日期:2016-07-19 出版日期:2017-02-20
通讯作者: 高永年,E-mail:yngao@niglas.ac.cn E-mail:yngao@niglas.ac.cn
作者简介:李沁(1993~),男,硕士研究生,主要从事土地利用变化GIS模拟研究.E-mail:chenwaigub@163.com
基金资助:
国土资源部重点地区土地综合承载力调查评价项目（DCPJ131208-01）；江苏省国土科技项目（201204）

URBAN EXPANSION SIMULATION USING MODIFIED PARTICLE SWARM OPTIMIZATION ALGORITHM AND CELLULAR AUTOMATA: A CASE STUDY OF NANJING CITY

LI Qin^1,2, SHEN Ming^1,2, GAO Yong-nian¹, ZHANG Zhi-fei³

1. Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, CAS, Nanjing 210008, China;
2. University of the Chinese Academy of Sciences, Beijing 100049, China;
3. Jiangsu Institute of Land Surveying and Planning, Nanjing 210024, China

Received:2016-05-23 Revised:2016-07-19 Online:2017-02-20
Supported by:
Land Carrying Capacity Survey and Evaluation Project of the Ministry of Land and Resources in Key Regions (DCPJ131208-01);Land, Resources, Science and Technology Projects of Jiangsu Province (201204)

摘要/Abstract

摘要： 为合理利用多智能体算法解决城市扩张动态模拟问题，基于地理学理论和社会学规律对粒子群算法进行有针对性的改进，提出分段式粒子群算法（SPSO），并结合元胞自动机模拟复杂时空过程的能力，构建出适用于城市扩张模拟的地理元胞自动机SPSO-CA。在SPSO-CA中我们利用多时像的土地利用数据、交通路网数据和地形数据，挖掘出1995~2000年南京城市扩张的土地转换规则。再由此规则实现1995~2008年的南京市城市扩张过程的动态模拟。最后对比SPSO-CA、PSO-CA及NULL模型结果得：SPSO-CA总精度86.3%，Kappa系数为0.792，Moran's I为0.078，PSO-CA总精度83.6%，Kappa系数为0.755，Moran's I为0.054，NULL模型总精度81.9%，Kappa系数为0.741，真实的Moran's I为0.072。这表明无论是总精度还是空间一致性，SPSO-CA都优于PSO-CA和NULL模型，即用SPSO-CA模拟城市扩张是可行的。

关键词: 粒子群算法, 元胞自动机, 城市扩张, 土地利用, GIS, 南京

Abstract: For scientific use of multi-agent algorithm to model dynamic urban growth, Subsection Particle Swarm Optimization (SPSO), an improved algorithm has been proposed in this paper. The improvement is based on the general rule in geography and sociology. Cellular Automata is also combined to simulate complex spatial-temporal processes. An new Geographic Cellular Automata (SPSO-CA) is constructed to achieve the dynamic simulation of urban growth. Deriving transition rules is key to the Geographic Cellular Automata. We therefore discover first the transition rules for SPSO-CA based on 1995-2000 land use data, traffic network data and terrain data. And then, dynamic simulation of urban expansion process of Nanjing City from 1995 to 2008 is made according to this rule. Lastly, in order to test the effectiveness of the improved algorithm, we compared SPSO-CA, PSO-CA and NULL model, the following results were obtained. The overall accuracy of SPSO-CA is 86.3%, with Kappa coefficient of 0.792, Moran's I of 0.078; the overall accuracy of PSO-CA is 83.6%, with Kappa coefficient of 0.755, actual Moran's I of 0.054; the overall accuracy of NULL model is 81.9%, with Kappa coefficient of 0.741, with actual Moran's I of 0.072. These results demonstrate that SPSO-CA is better than PSO-CA and NULL model and the improvement of Subsection Particle Swarm Optimization is available.

Key words: particle swarm optimization, cellular automata, land use, urban expansion, GIS, Nanjing

中图分类号:

P209

李沁, 沈明, 高永年, 张志飞. 基于改进粒子群算法和元胞自动机的城市扩张模拟——以南京为例[J]. 长江流域资源与环境, 2017, 26(02): 190-197.

LI Qin, SHEN Ming, GAO Yong-nian, ZHANG Zhi-fei. URBAN EXPANSION SIMULATION USING MODIFIED PARTICLE SWARM OPTIMIZATION ALGORITHM AND CELLULAR AUTOMATA: A CASE STUDY OF NANJING CITY[J]. RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN, 2017, 26(02): 190-197.

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