Detecting pattern in biological stressor response relationships using model based cluster analysis

Environmental monitoring of aquatic systems is needed to estimate the quality of the systems, to evaluate standards and to study stressor–response relationships. Monitoring programs often focus on the collection of biological, chemical and physical measures of the system. An important concern is the effect of chemical and physical stressors on the biological community. Evaluation of relationships may be difficult as the extent of the relationship is not known. From a management perspective, interest is on what factors affect the biological community and where these factors have an influence. The focus of this paper is on the use of regression based cluster analysis as a tool for finding relationships between a single biological response and a suite of environmental stressors. The approach to cluster analysis uses a penalized regression classification likelihood and Markov Chain Model Composition Monte Carlo. This approach allows for simultaneous development of regression models and clustering of the regression models. The method is applied to the analysis of a data set describing stressors/response relationship in Ohio.     

Efficient implementation of the Metropolis-Hastings algorithm,with application to the Cormack–Jolly–Seber model

Judicious choice of candidate generating distributions improves efficiency of the Metropolis-Hastings algorithm. In Bayesian applications, it is sometimes possible to identify an approximation to the target posterior distribution; this approximate posterior distribution is a good choice for candidate generation. These observations are applied to analysis of the Cormack–Jolly–Seber model and its extensions.     

基于马尔可夫链的城市火灾预测

根据1982-2001年的城市火灾统计资料,对现有火灾发生率进行分级,应用马尔可夫链对2002年和2005年城市火灾等级进行了预测,根据这种方法又对1992-2001年的火灾发生率进行检验,比较其预测结果与实际结果的一致性。     

Temporal and spatial change detecting (1998–2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images

Land use/land cover (LULC) has a profound impact on economy, society and environment, especially in rapid developing areas. Rapid and prompt monitoring and predicting of LULC’s change are crucial and significant. Currently, integration of Geographical Information System (GIS) and Remote Sensing (RS) methods is one of the most important methods for detecting LULC’s change, which includes image processing (such as geometrical-rectifying, supervised-classification, etc.), change detection (post-classification), GIS-based spatial analysis, Markov chain and a Cellular Automata (CA) models, etc. The core corridor of Pearl River Delta was selected for studying LULC’s change in this paper by using the above methods for the reason that the area contributed 78.31% (1998)–81.4% (2003) of Gross Domestic Product (GDP) to the whole Pearl River Delta (PRD). The temporal and spatial LULC’s changes from 1998 to 2003 were detected by RS data. At the same time, urban expansion levels in the next 5 and 10 years were predicted temporally and spatially by using Markov chain and a simple Cellular Automata model respectively. Finally, urban expansion and farmland loss were discussed against the background of China’s urban expansion and cropland loss during 1990–2000. The result showed: (1) the rate of urban expansion was up to 8.91% during 1998–2003 from 169,078.32 to 184,146.48 ha; (2) the rate of farmland loss was 5.94% from 312,069.06 to 293,539.95 ha; (3) a lot of farmland converted to urban or development area, and more forest and grass field converted to farmland accordingly; (4) the spatial predicting result of urban expansion showed that urban area was enlarged ulteriorly compared with the previous results, and the directions of expansion is along the existing urban area and transportation lines.     

Using accelerometer,high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model

The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal species.     

A stochastic forest fire growth model

We consider a stochastic fire growth model, with the aim of predicting the behaviour of large forest fires. Such a model can describe not only average growth, but also the variability of the growth. Implementing such a model in a computing environment allows one to obtain probability contour plots, burn size distributions, and distributions of time to specified events. Such a model also allows the incorporation of a stochastic spotting mechanism.

基于碳排放约束的土地利用结构优化模型研究及其应用

以经济快速发展区域（县域）的土地利用生态经济系统为研究对象,利用系统科学相关分析方法,结合区域土地利用的经济、生态和社会功能变化趋势,在分析区域未来社会经济科学发展、碳排放目标与土地利用结构变化关系的基础上,研究了土地利用结构调控的CO2当量指数、经济效益指数及其目标函数,通过集成Markov模型和结构优化方法,建立了区域土地利用结构的低碳优化动态调控模型（LUSCC）及其求解方法。最后以江苏泰兴市为例进行了分析验证,综合考虑该区域的发展战略和政策实施情况,获得3种不同碳排放目标调控程度的土地利用优化结构及其调控方法,研究表明,建立的基于碳排放约束的动态优化调控模型,能够满足土地资源配置效率的最大化及其可持续利用要求,为探索区域未来低碳型土地利用结构的优化方法提供参考。     

基于马尔可夫链的四川省产业结构时空演变

本文利用2000-21007年闻四川省158个县域的产业结构系数,基于传统和空闻马尔可夫链方法,分别构建县域产业结构的非空间和空间马尔可夫转移概率矩阵.对"西部大开发"战略实施以来四川省县域产业结构的时空动态演变特征进行初步分析.首先按照全省产业结构系数的平均水平,将所有县域划分为低、中低、中高和高4种类型,并构建其马尔可夫转移概率矩阵,考察全省县域尺度上的产业结构水平是否存在趋同现象;其次,以每个县域在初始年份的空间滞后类型为条件,构造其空闫马尔可夫转移概率矩阵,分析在不同地理背景影响下四川省县域产业结构的时空演变特征;最后,通过县域产业结构系数的遗历分布,预测四川省县域产韭结构水平的长期发展趋势.结果表明:①2000-2007年间四川省县域尺度上的产业结构存在一定程度的趋同现象,并未发生两极分化:②四川省县域产业结构系数的类型转移受地理背景的影响明显,同一类型区域在不同背景下的转移概率发生显著变化;③在不同区域背景下,四川省县域产业结构水平的长期发展趋势各有不同,以在中低水平背景下的区域发展潜力最大.     

重大危险源风险评价研究

运用马尔可夫过程研究了化工企业重大危险源事故发生的概率,在基于马尔可夫事故概率假设模型的基础上分析了化工企业事故概率数学模型.对重大危险源事故易发性、事故严重度、固有危险性及危险等级进行了评价,并进行了实例计算.     

改进的灰色马尔科夫模型在飞行事故率预测中的应用

基于灰色理论和马尔科夫理论,建立传统的灰色预测模型和灰色马尔科夫预测模型;对传统灰色预测模型的背景值和初值的构造存在一定的误差进行改进,并用改进后的新模型对飞行事故率预测的结果与传统的灰色马尔科夫模型进行对比;仿真结果表明:改进后的灰色马尔科夫模型预测精度有了进一步的提高并验证了算法的有效性。