Effects of crab–halophytic plant interactions on creek growth in a S.W. Atlantic salt marsh: A Cellular Automata model

The Bahía Blanca Estuary (38° 50′ S, and 62° 30′ W) presents salt marshes where interactions between the local flora (Sarcocornia perennis) and fauna (Chasmagnathus granulatus) generate some kind of salt pans that alter the normal water circulation and condition its flow and course towards tidal creeks. The crab–vegetation dynamics in the salt marsh presents variations that cannot be quantified in a reasonable period of time. The interaction between S. perennis plant and C. granulatus crab is based on simple laws, but its result is a complex biological mechanism that causes an erosive process on the salt marsh and favors the formation of tidal creeks. To study it, a Cellular Automata model is proposed, based on the laws deduced from the observation of these phenomena in the field, and then verified with measurable data within macroscale time units. Therefore, the objective of this article is to model how the interaction between C. granulatus and S. perennis modifies the landscape of the salt marsh and influences the path of tidal creeks. The model copies the basic laws that rule the problem based on purely biological factors.The Cellular Automata model proved capable of reproducing the effects of the interaction between plants and crabs in the salt marsh. A study of the water drainage of the basins showed that this interaction does indeed modify the development of tidal creeks. Model dynamics would likewise follow different laws, which would provide a different formula for the probability of patch dilation. The patch shape can be obtained changing the pattern that dilates.     

基于土地适宜性评价与元胞自动机模型的城市开发边界划定方法

城市开发边界的划定可以在一定程度上引导城市空间的良性扩张。为了研究边界划定的方法,以我国某港口城市作为研究区域,在空间增长模拟的方向进行探索。研究中借助土地适宜性评价和元胞自动机边界工具,构建既符合生态资源环境要求又符合城市发展需要的边界划定,同时,为了实现城市用地健康和动态管控举措,提出了建立健全土地用地管理机制的要求。     

A way to synchronize models with seismic faults for earthquake forecasting: Insights from a simple stochastic model

Numerical models are starting to be used for determining the future behaviour of seismic faults and fault networks. Their final goal would be to forecast future large earthquakes. In order to use them for this task, it is necessary to synchronize each model with the current status of the actual fault or fault network it simulates (just as, for example, meteorologists synchronize their models with the atmosphere by incorporating current atmospheric data in them). However, lithospheric dynamics is largely unobservable: important parameters cannot (or can rarely) be measured in Nature. Earthquakes, though, provide indirect but measurable clues of the stress and strain status in the lithosphere, which should be helpful for the synchronization of the models.The rupture area is one of the measurable parameters of earthquakes. Here we explore how it can be used to at least synchronize fault models between themselves and forecast synthetic earthquakes. Our purpose here is to forecast synthetic earthquakes in a simple but stochastic (random) fault model. By imposing the rupture area of the synthetic earthquakes of this model on other models, the latter become partially synchronized with the first one. We use these partially synchronized models to successfully forecast most of the largest earthquakes generated by the first model. This forecasting strategy outperforms others that only take into account the earthquake series. Our results suggest that probably a good way to synchronize more detailed models with real faults is to force them to reproduce the sequence of previous earthquake ruptures on the faults. This hypothesis could be tested in the future with more detailed models and actual seismic data.     

Cellular automata as analysis and synthesis engines at the geomorphology–ecology interface

The linkages between ecology and geomorphology can be difficult to identify because of physical complexity and the limitations of the current theoretical representations in these two fields of study. Deep divisions between these disciplines are manifest in the methods used to simulate process, such as rigidly physical-deterministic methods for many aspects of geomorphology compared with purely stochastic simulations in many models of change in landcover. Practical and theoretical research into ecology–geomorphology linkages cannot wait for a single simulation schema which may never come; as a result, studies of these linkages often appear disjointed and inconsistent.The grid-based simulation framework for cellular automata (CA) allows simultaneous use of competing schemas. CA use in general geographic studies has been primarily limited to urban simulations models of change for land cover, both highly stochastic and/or expert rule-based. In the last decade, however, methods for describing physically deterministic systems in the CA framework have become much more accurate. The possibility now exists to merge separate CA simulations of different environmental systems into unified “multiautomata” models. Because CAs allow transition rules that are deterministic, probabilistic, or expert rule-based, they can immediately incorporate the existing knowledge rules in ecology and geomorphology. The explicitly spatial nature of CA provides a map-like framework that should allow a simple and deeply rooted connection with the mapping traditions of the geosciences and ecological sciences.     

基于神经网络的单元自动机CA及真实和忧化的城市模拟

提出了一种基于神经网络的单元自动机（ＣＡ）。ＣＡ已被越来越多地应用在城市及其它地理现象的模拟中。ＣＡ模拟所碰到的最大问题是如何确定模型的结构和参数。模拟真实的城市涉及到使用许多空间变量和参数。当模型较复杂时，很难确定模型的参数值。本模型的结构较简单，模型的参数能通过对神经网络的训练来自动获取。分析表明，所提出的方法能获得更高的模拟精度，并能大大缩短寻找参数所需要的时间。通过筛选训练数据，本模型还可以进行优化的城市模拟，为城市规划提供参考依据。     

基于遥感与GIS的20世纪90年代中国城镇用地时空特征

文章利用20世纪90年代初期、中期和末期全国1:100000土地利用动态变化数据提取城镇用地动态变化数据,利用单元自动机和人工神经网络模型对全国城镇用地进行了区划.在此基础上,研究了90年代两个阶段中国城镇用地时空格局.研究表明:90年代前5年东部沿海地区受经济高速发展和开放政策的影响,城镇用地扩展迅速,中西部地区城镇用地扩展较慢;90年代后5年国家加大了耕地资源保护力度,在政府宏观调控政策和耕地资源保护条例的影响下,东部沿海地区城镇用地扩展大幅回落,中部地区城镇扩展也有较大幅度回落,西部地区随着经济发展加快,城镇用地扩展回落较小.     

基于最大发展潜力值的元胞自动机城市扩张模型

在城镇化发展水平及土地开发评价的基础上,评价待扩张区域土地转变潜力分值,基于最大转变潜力分值,提出一种有别于传统元胞自动机模拟城市扩张的新方法。该方法与传统元胞自动机模拟城市扩张相比在迭代方式上存在不同,它克服了传统元胞自动机因阈值设置不同而导致结果不确定的难题。分别将该方法与传统元胞自动机模型应用于武汉主城区,模拟了其2003年到2013年的城市扩张情况,最后,将模拟结果与实际土地利用现状图进行对比发现改进后的新方法在模拟精度上大大提高。     

基于SDOG-ESSG的地球系统元胞自动机框架及其应用试验

元胞自动机结构简单且具备模拟复杂系统的能力,已被广泛应用于大气、流体力学、地球物理等领域。然而,现有元胞自动机以欧氏空间为约束进行地球系统过程模拟,忽略了地球重力等天然约束,导致计算过程中元胞状态的传递方向与真实运动的趋势方向不相符,一定程度上扭曲了最终的模拟结果。本文提出了地球系统元胞自动机这一概念,并从元胞表达及构建、邻居模型等方面设计了基于SDOG-ESSG格网的地球系统元胞自动机框架。由于演化规则取决于不同的应用,因此本文进一步以地壳的热传递为例,通过对热力学方程离散化设计了相应的演化规则。最后,借助公开的数据源开展了地壳热传递元胞自动机模拟的初步试验,并与一定区域下的数值模拟结果展开了比对。试验表明,与数值模拟方法相比,本文方法的模拟结果相对误差控制在27%以内,具备一定程度的可行性,可作为地球系统过程模拟的一种新思路。     

Integrating cellular automata,artificial neural network,and fuzzy set theory to simulate threatened orchards: application to Maragheh,Iran

Urbanization processes challenge the growth of orchards in many cities in Iran. In Maragheh, orchards are crucial ecological, economical, and tourist sources. To explore orchards threatened by urban expansion, this study first aims to develop a new model by coupling cellular automata (CA) and artificial neural network with fuzzy set theory (CA–ANN–Fuzzy). While fuzzy set theory captures the uncertainty associated with transition rules, the ANN considers spatial and temporal nonlinearities of the driving forces underlying the urban growth processes. Second, the CA–ANN–Fuzzy model is compared with two existing approaches, namely a basic CA and a CA coupled with an ANN (CA–ANN). Third, we quantify the amount of orchard loss during the last three decades as well as for the upcoming years up to 2025. Results show that CA–ANN–Fuzzy with 83% kappa coefficient performs significantly better than conventional CA (with 51% kappa coefficient) and CA–ANN (with 79% kappa coefficient) models in simulating orchard loss. The historical data shows a considerable loss of 26% during the last three decades, while the CA–ANN–Fuzzy simulation reveals a considerable future loss of 7% of Maragheh’s orchards in 2025 due to urbanization. These areas require special attention and must be protected by the local government and decision-makers.     

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.