基于粒子群算法的矢量元胞自动机转换规则获取

本文提出了一种基于粒子群算法来自动获取矢量元胞自动机转换规则的新方法。采用粒子群算法所提取的转换规则毋需通过数学公式来表达,能更方便和准确地描述自然界中的复杂关系,并且这些规则比数学公式更容易让人理解。以丹阳市城市扩展为例,使用粒子群算法挖掘元胞自动机转换规则模拟该研究区域的城市扩展过程,并对模拟结果进行了精度评价。     

Fisher判别及自动获取元胞自动机的转换规则

提出一种基于费歇尔(Fisher)判别和离散选择模型相结合来自动获取地理元胞自动机转换规则的方法。CA的核心是如何定义转换规则,但目前主要是采用启发式的方法来定义转换规则,受主观因素影响较大。本模型结合离散选择模型,通过对Fisher判别方法进行改进,可以成功搜索最佳分隔单元发展和不发展的变量组合,自动确定模型参数值。与常用的Logistic回归模型进行对比分析,结果表明,所提出的方法具有更高的模拟精度,转换规则有着清晰的物理意义。此外,本模型在模拟多类复杂的土地利用变化时可能更具有优势。     

耦合遥感观测和元胞自动机的城市扩张模拟

在传统元胞自动机(CA)模型中,静态的模型参数和模型误差不能释放是影响城市扩张模拟效果的两个重要原因。文中引入集合卡尔曼滤波方法到CA模型中,提出了基于联合状态矩阵的地理元胞自动机。该模型在模拟过程中可以通过同化遥感观测数据,动态地调整模型参数和纠正模拟结果,使模型参数能够反映转换规则的时空变化,同时也能较好地释放积累的模型误差。将模型应用于东莞市的城市扩张模拟中,实验结果表明,模型能够准确地调整模型参数使之符合城市发展模式,同时也能有效地控制模型误差,其模拟的空间格局与真实情况吻合。     

一种基于机器学习的城市发展模拟元胞模型

利用元胞自动机(Cellular Automata,CA)模拟土地利用变化,已经成为认识和理解其复杂动态演化过程的有效手段.传统的元胞自动机基于线性转换规则,较难表达土地利用变化的非线性边界问题.本文研究利用最小二乘支持向量机方法(LS-SVM),将原空间下的非线性可分问题,通过高斯径向基核函数映射到高维特征空间,简化...     

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。     

元胞空间分区及其对GeoCA模型模拟精度的影响

采用双约束空间聚类方法对元胞空间进行分区,在此基础上对不同的分区分别求取元胞转换规则,从而提高 元胞自动机的模拟精度。以杭州市土地利用变化为例,采用本文提出的基于双约束空间聚类的分区元胞自动机模型对 研究区域2000年—2005年的土地利用变化进行模拟,并利用逐点对比法和Moran I指数对模拟结果进行精度评估。结果 表明：(1)采用双约束空间聚类算法对元胞空间进行分区,可以保证同一分区内的元胞既在空间上邻近,又具有相对一 致的非空间属性信息,分区效果较好;(2)与不分区元胞自动机模型和基于空间聚类的分区元胞自动机模型相比,双约 束空间聚类元胞自动机模型具有较高的模拟精度,尤其是在空间形态和整体结构上具有较好的模拟效果。     

利用元胞自动机计算河道槽蓄量

构建基于元胞自动机的河道水流漫延模型,在该模型中针对河道地形特点处理边界问题,基于水力学中的曼宁公式构建模型局部转换规则,并利用元胞自动机的模拟空间复杂系统动态演变能力的特点,模拟了水流由上断面向下断面流动的动态过程,形成符合上下断面水位的水面,进而计算河道槽蓄量.实验结果表明,把元胞自动机模型引入水文领域计算河道槽蓄量的方法具有可行性.     

基于数据同化的元胞自动机

提出基于集合卡尔曼滤波(EnKF)的元胞自动机(CA)模型。在CA模型中,由于不同的样本会训练出不同参数值 的转换规则,且获取的转换规则在整个模拟过程中不能改变等原因,误差在模拟过程中会不断累积。本文在CA模型中 引入集合卡尔曼滤波的数据同化方法,建立了基于集合卡尔曼滤波的数据同化CA模型,同化遥感观测数据,根据得出 的同化值修正模拟结果使之向真实情况逼近。利用该模型模拟了广东省东莞市的发展情景(1995年—2005年),实验表 明,与传统CA模型相比,基于集合卡尔曼滤波的CA模型能够融合遥感观测数据,并能更有效地模拟城市扩张过程,达 到良好的模拟效果。     

MonoLoop:CA城市模型状态转换规则获取的一种方法

状态转换规则是元胞自动机(Cellular Automata,CA)的核心,如何获取并建立CA的状态转换规则是构建CA模型的关键。邻域作用是CA能够模拟复杂物理现象的核心驱动力,而在已有的用于城市增长模拟的CA城市模型中,因为邻域作用在模拟的过程中为时间动态的变量,其系数很难通过常用的Logistic回归方法识别,致使已有的CA城市模型的状态转换规则中,往往仅通过Logistic回归获取邻域作用之外的空间变量的模型参数,而邻域作用的参数通常采用主观赋值的方法。本文提出了CA城市模型的多指标评价(Multi-Criteria Evalua-tion,MCE)形式状态转换规则获取的一种新方法 MonoLoop,并针对北京市域1976～2006年的城市增长开展了该方法的实验。基于这种方法,一方面利用历史数据可以建立更为客观的状态转换规则;另一方面也可以大大降低模型参数识别的时间。     

基于C5.0决策树算法的元胞自动机的洪河湿地演化模拟

以洪河自然保护区1992年、2001年、2010年三期TM遥感影像为数据源,利用C5.0决策树算法从已有的数据及其影响因子数据中挖掘出洪河湿地的演变规则,并将获得的转换规则应用到元胞自动机模型中进行洪河湿地演变的动态模拟与预测,分析和探讨了元胞自动机模型在湿地景观模拟和预测中的重要作用。结果表明,在现有的空间变量和条件不变的情况下,在未来的洪河自然保护区湿地面积将减小,洪河自然保护区干旱化将加重。通过对湿地景观的动态变化模拟和预测研究,能够较好地反映湿地景观的动态变化情况。     

Integration of neural networks and cellular automata for urban planning

This paper presents a new type of cellular automata (CA) model for the simulation of alternative land development using neural networks for urban planning. CA models can be regarded as a planning tool because they can generate alternative urban growth. Alternative development patterns can be formed by using different sets of parameter values in CA simulation. A critical issue is how to define parameter values for realistic and idealized simulation. This paper demonstrates that neural networks can simplify CA models but generate more plausible results. The simulation is based on a simple three-layer network with an output neuron to generate conversion probability. No transition rules are required for the simulation. Parameter values are automatically obtained from the training of network by using satellite remote sensing data. Original training data can be assessed and modified according to planning objectives. Alternative urban patterns can be easily formulated by using the modified training data sets rather than changing the model.     

Integration of neural networks and cellular automata for urban planning

This paper presents a new type of cellular automata (CA) model for the simulation of alternative land development using neural networks for urban planning. CA models can be regarded as a planning tool because they can generate alternative urban growth. Alternative development patterns can be formed by using different sets of parameter values in CA simulation. A critical issue is how to define parameter values for realistic and idealized simulation. This paper demonstrates that neural netowrks can simplify CA models but generate more plausible results. The simulation is based on a simple three-layer network with an output neuron to generate conversion probability. No transition rules are required for the simulation. Parameter values are automatically obtained from the training of network by using satellite remote sensing data. Original training data can be assessed and modified according to planning objectives. Alternative urban patterns can be easily formulated by using the modified training data sets rather than changing the model.     

集成GIS和细胞自动机模型进行地理时空过程模拟与预测的新方法

目前商用地理信息系统（GIS）不能完整地表达地理实体的时态信息和时空关系，缺乏时空分析和时空动态模拟的能力，这已成为GIS界的一个共识，然而，未来GIS在各应用领域的深入发展以及在实现“数字地球”战略过程中，都要求发展新的时空分析和模拟方法，细胞自动机（Cellular Automaton)是一种“自下而上”的动态模拟建模框架，具有模拟地理复要系统时空演化过程的能力，首先将标准CA模型的4元组进行扩展以满足GIS环境下时空动态模型的要求，然后以城市土地利用演化这一动态过程为例，建立了土地利用演化动态模拟与预测模型（LESP），最后运用此模型对包头市城市扩展和土地可持续利用演化进行了比较成功的模拟和预测。     

Deriving urban dynamic evolution rules from self-adaptive cellular automata with multi-temporal remote sensing images

Cellular automata (CA) have proven to be very effective for simulating and predicting the spatio-temporal evolution of complex geographical phenomena. Traditional methods generally pose problems in determining the structure and parameters of CA for a large, complex region or a long-term simulation. This study presents a self-adaptive CA model integrated with an artificial immune system to discover dynamic transition rules automatically. The model’s parameters are allowed to be self-modified with the application of multi-temporal remote sensing images: that is, the CA can adapt itself to the changed and complex environment. Therefore, urban dynamic evolution rules over time can be efficiently retrieved by using this integrated model. The proposed AIS-based CA model was then used to simulate the rural-urban land conversion of Guangzhou city, located in the core of China’s Pearl River Delta. The initial urban land was directly classified from TM satellite image in the year 1990. Urban land in the years 1995, 2000, 2005, 2009 and 2012 was correspondingly used as the observed data to calibrate the model’s parameters. With the quantitative index figure of merit (FoM) and pattern similarity, the comparison was further performed between the AIS-based model and a Logistic CA model. The results indicate that the AIS-based CA model can perform better and with higher precision in simulating urban evolution, and the simulated spatial pattern is closer to the actual development situation.     

利用生物地理学优化算法获取城市扩展元胞自动机模型参数

提出了一种基于生物地理学优化算法寻找城市扩展元胞自动机（cellular automata,CA）模型最佳参数的方法。转换规则制定及相应权重参数获取是构建城市扩展CA的核心和难点。生物地理学优化算法（biogeography-based optimization,BBO）通过模拟生物物种在栖息地的分布、迁移和灭绝来求解优化问题。利用BBO算法自动获取城市扩展CA模型参数值,构建BBO-CA模型进行城市扩展模拟实验,并与粒子群算法（particle swarm optimization,PSO）、蚁群算法（ant colony optimization,ACO）、遗传算法（genetic algorithm,GA）及逻辑回归（logistic regression,LR）等方法相比较。结果表明,BBO算法具有较好的收敛性,可有效地快速自动寻找城市扩展CA模型最佳参数组合,获取的空间变量权重参数较为合理;BBO-CA模型明显提升了城市用地模拟精度,城市用地模拟精度为72.5%,相对PSO、ACO、GA、LR各算法分别提升了1.1%、1.2%、2.7%和4.0%,Kappa系数达到0.700,分别提升了0.015、0.016、0.034和0.046,且整体空间布局与实际情况更为接近,验证了应用BBO算法的可行性与优势。     

Improving urban cellular automata performance by integrating global and geographically weighted logistic regression models

In many of the conventional cellular automata (CA) models, particularly Urban‐CA which are used for urban growth, the spatial heterogeneities and local differences of the land use conversion processes are ignored. Global logistic regression (LR) is a popular model employed to define the transition rules of Urban‐CA. By considering the local characteristics, Geographically Weighted Logistic Regression (GWLR) provides interesting capabilities for urban growth modelling. In this research, in addition to using GWLR in the definition of transition rules, the advantages of integrating GWLR and LR for urban growth simulation were evaluated; these have not been considered in previous studies. Local and global probabilities obtained from the calibration of GWLR and LR were combined to define the transition rules of an Urban‐CA. Urban growth was simulated in the Islamshahr sub‐region located southwest of Tehran, Iran for the two periods 1992‐1996 and 1996‐2002, and data from these periods were used for training and testing the prediction abilities, respectively. In the first period, GWLR showed good performance and a significant contribution to the enhancement of the simulation performance, but in the second period, the effectiveness of LR on the prediction accuracy increased. Due to their complementary roles, the integration of the GWLR and LR models resulted in improved simulation performance in both periods.     

土地利用变化研究中的细胞自动机与灰色局势决策

把细胞自动机和灰色局势决策结合起来对土地利用变换机制进行模拟。实验证明,基于灰色局势决策规则的细胞自动机是对土地利用变换机制从宏观和微观角度进行模拟的有效方法。     

A multi‐label cellular automata model for land change simulation

The use of cellular automata (CA) has for some time been considered among the most appropriate approaches for modeling land‐use changes. Each cell in a traditional CA model has a state that evolves according to transition rules, taking into consideration its own and its neighbors’ states and characteristics. Here, we present a multi‐label CA model in which a cell may simultaneously have more than one state. The model uses a multi‐label learning method—a multi‐label support vector machine, Rank‐SVM—to define the transition rules. The model was used with a multi‐label land‐use dataset for Luxembourg, built from vector‐based land‐use data using a method presented here. The proposed multi‐label CA model showed promising performance in terms of its ability to capture and model the details and complexities of changes in land‐use patterns. Applied to historical land use data, the proposed model estimated the land use change with an accuracy of 87.2% exact matching and 98.84% when including cells with a misclassification of a single label, which is comparably better than a classical multi‐class model that achieved 83.6%. The multi‐label cellular automata outperformed a model combining CA and artificial neural networks. All model goodness‐of‐fit comparisons were quantified using various performance metrics for predictive models.     

Modelling coastal land use change by incorporating spatial autocorrelation into cellular automata models

This paper presents a spatial autoregressive (SAR) method-based cellular automata (termed SAR-CA) model to simulate coastal land use change, by incorporating spatial autocorrelation into transition rules. The model captures the spatial relationships between explained and explanatory variables and then integrates them into CA transition rules. A conventional CA model (LogCA) based on logistic regression (LR) was studied as a comparison. These two CA models were applied to simulate urban land use change of coastal regions in Ningbo of China from 2000 to 2015. Compared to the LR method, the SAR model yielded smaller accumulated residuals that showed a random distribution in fitting the CA transition rules. The better-fitting SAR model performed well in simulating urban land use change and scored an overall accuracy of 85.3%, improving on the LogCA model by 3.6%. Landscape metrics showed that the pattern generated by the SAR-CA model has less difference with the observed pattern.