Integrating the system dynamic and cellular automata models to predict land use and land cover change

Land use and land cover change (LULCC) is a widely researched topic in related studies. A number of models have been established to simulate LULCC patterns. However, the integration of the system dynamic (SD) and the cellular automata (CA) model have been rarely employed in LULCC simulations, although it allows for combining the advantages of each approach and therefore improving the simulation accuracy. In this study, we integrated an SD model and a CA model to predict LULCC under three future development scenarios in Northern Shanxi province of China, a typical agro-pastoral transitional zone. The results indicated that our integrated approach represented the impacts of natural and socioeconomic factors on LULCC well, and could accurately simulate the magnitude and spatial pattern of LULCC. The modeling scenarios illustrated that different development pathways would lead to various LULCC patterns. This study demonstrated the advantages of the integration approach for simulating LULCC and suggests that LULCC is affected to a large degree by natural and socioeconomic factors.     

A computational model of the digestive gland epithelial cell of marine mussels and its simulated responses to oil-derived aromatic hydrocarbons

This paper describes a computational model of digestive gland epithelial cells (digestive cells) of marine mussels. These cells are the major environmental interface for uptake of contaminants, particularly those associated with natural particulates that are filtered from seawater by mussels. Digestive cells show well characterised reactions to exposure to lipophilic xenobiotics, such as oil-derived aromatic hydrocarbons (AHs), which accumulate in these cells with minimal biotransformation. The simulation model is based on processes associated with the flux of carbon through the cell. Physiological parameters such as fluctuating food concentration, cell volume, respiration, secretion/excretion, storage of glycogen and lipid, protein/organelle turnover (autophagy/resynthesis) and export of carbon to other tissues of the mussel are all included in the model. The major response to AHs is induction of increased autophagy in these cells. Simulations indicate that the reactions to AHs and food deprivation correspond well with responses measured in vivo.     

有无1墨水点亚对数空间限定交替式下推自动机之间的关系

该文引入 1墨水点 2方向交替式下推自动机 ,它是 1个具有额外能力的 2方向交替式下推自动机 ,能够用 1个墨水点在输入带上标记出最多 1个单元格。对具有 1个墨水点的和没有墨水点的亚对数空间限定交替式下推自动机之间的关系进行研究。实例证明了具有 1个墨水点的亚对数空间限定交替式下推自动机的语言受理能力强于没有墨水点的亚对数空间限定交替式下推自动机     

Using a maximum entropy model to optimize the stochastic component of urban cellular automata models

ABSTRACT

The stochastic perturbation of urban cellular automata (CA) model is difficult to fine-tune and does not take the constraint of known factors into account when using a stochastic variable, and the simulation results can be quite different when using the Monte Carlo method, reducing the accuracy of the simulated results. Therefore, in this paper, we optimize the stochastic component of an urban CA model by the use of a maximum entropy model to differentially control the intensity of the stochastic perturbation in the spatial domain. We use the kappa coefficient, figure of merit, and landscape metrics to evaluate the accuracy of the simulated results. Through the experimental results obtained for Wuhan, China, the effectiveness of the optimization is proved. The results show that, after the optimization, the kappa coefficient and figure of merit of the simulated results are significantly improved when using the stochastic variable, slightly improved when using Monte Carlo methods. The landscape metrics for the simulated results and actual data are much closer when using the stochastic variable, and slightly closer when using the Monte Carlo method, but the difference between the simulated results is narrowed, reflecting the fact that the results are more reliable.     

Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata

ABSTRACT

Vector-based cellular automata (VCA) models have been applied in land use change simulations at fine scales. However, the neighborhood effects of the driving factors are rarely considered in the exploration of the transition suitability of cells, leading to lower simulation accuracy. This study proposes a convolutional neural network (CNN)-VCA model that adopts the CNN to extract the high-level features of the driving factors within a neighborhood of an irregularly shaped cell and discover the relationships between multiple land use changes and driving factors at the neighborhood level. The proposed model was applied to simulate urban land use changes in Shenzhen, China. Compared with several VCA models using other machine learning methods, the proposed CNN-VCA model obtained the highest simulation accuracy (figure-of-merit = 0.361). The results indicated that the CNN-VCA model can effectively uncover the neighborhood effects of multiple driving factors on the developmental potential of land parcels and obtain more details on the morphological characteristics of land parcels. Moreover, the land use patterns of 2020 and 2025 under an ecological control strategy were simulated to provide decision support for urban planning.     

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.     

细胞自动机的多重分形特征和动力学根源

用大量弹簧滑块组成的模型可以模拟地震的复杂性，本文以细胞自动机（ＣＡ）为数学工具，结合地震学和岩石力学的结果，考虑了能量的输入和损耗，进行了地震事件的数字模拟。对模型中输出的“地震”事件进行了时间和空间的多分形分析，并对自组织临界（ＳＯＣ）状态进行了讨论，进而得到事件至少在时间域里的分布呈现出层次结构，符合自相似特征。本文的ＣＡ模型属于确定性的混沌系统，而对于处在ＳＯＣ状态下的大量事件的频度和大小的统计与Ｇ－Ｒ指数律很好地吻合。结果表明，虽然自组织（ＳＯ）可能是更准确的地震表征，但ＳＯＣ仍然是研究地震现象的很好的模式。     

细胞自动机在地震波传播研究中的应用

本文基于细胞自动机在地震波传播研究中的最近成果，阐述了其基本的研究思路与理论框架，给出了一个计算实例．在此基础上就细胞自动机方法与传统波动方程的衔接关系及复杂介质中地震波正反问题作了讨论，并对研究前景进行展望．     

Modeling urban expansion scenarios by coupling cellular automata model and system dynamic model in Beijing, China

Spatially explicit urban expansion models that can trace urban development in the past and predict the expansion scenarios in the future are indispensable for examining urban planning policies. This paper demonstrates a new urban expansion scenario (UES) model by coupling one “bottom-up” cellular automata (CA)-based model and one “top-down” system dynamics (SD)-based model. By implementing the UES model in Beijing, the urban evolution from 1991 to 2004 was simulated and the UESs from 2004 to 2020 were predicted. The results suggest that a dilemma of urban expansion versus limited water resource and environment deterioration exists. Dealing with such a dilemma remains a challenge for the local government.     

基于支持向量机的元胞自动机及土地利用变化模拟

提出了利用遥感数据，并采用支持向量机来确定元胞自动机非线性转换规则的新方法。元胞自动机在模拟复杂地理现象时，需要采用非线性转换规则。目前元胞自动机主要采用线性方法来获取转换规则，在反映复杂的非线性地理现象时有一定的局限性。以城市扩张的模拟为例，将模拟城市系统的主要特征变量映射到Hilbert空间后，通过SVM建立最优分割超平面，分割超平面的分类决策函数由径向基核（Radial Basis Kernel）构造。利用历史遥感数据校正超平面的决策函数，确定城市元胞自动机的非线性转换规则，计算出城市发展概率。利用所提出的方法，对深圳市1988-2010年的城市发展进行了模拟，取得了较理想的模拟效果。研究结果表明，基于SVM-CA模型的模拟精度比传统MCE方法模拟精度高，MoranⅠ指数与实际更为接近。