经济发达地区土地利用结构变化预测 --以江苏省江阴市为例

结构决定功能，把握土地利用结构变化的趋势，调整社会经济发展的策略。优化土地利用结构是土地可持续利用的关键。以经济发达地区江苏省江阴市为例，运用马尔柯夫链的理论与方法，构建土地利用结构转移的概率矩阵，预测江阴市2005年和2010年的土地利用结构，并分析其变化特征，以此来研究经济发达地区近期内可能的土地利用结构变化规律。模拟结果表明，经济发达地区土地利用结构变化存在着非农化趋势加快．农地数量减少加速的现象，如果为保证耕地的数量平衡，则可能导致农地内部结构极其不合理。根据研究结果，提出适当释放农地，促进经济发展；运用经济杆杠，提高非农用地利用效率；重视耕地质量，改善生态环境的政策建议。     

Spatial modelling of the probability of obtaining a detectable concentration of ammonia in Moreton Bay, Australia

Ammonia is an important water quality variable, which in excess, can be detrimental to waterways and their ecosystems. In the Ecosystem Health Monitoring Program in South-east Queensland ammonia is monitored monthly, however, often more than 50% of the ammonia observations in Moreton Bay are below detection limit, making it difficult to draw useful inferences. In this paper a clipped Gaussian random field is used to spatially model and map the probability of detectable concentration of ammonia. The methodology is applied to the Moreton Bay samples collected in February 2005. The results suggest that for this month the oceanic impacted areas have higher probability of detectable ammonia concentration than the areas closer to the main sources of anthropogenic inputs.     

Implications of Land Use Changes on Carbon Dynamics and Sequestration—Evaluation from Forestry Datasets, India

Forests and soils are a major sink of carbon, and land use changes can affect the magnitude of above ground and below ground carbon stores and the net flux of carbon between the land and the atmosphere. Studies on methods for examining the future consequences of changes in patterns of land use change and carbon flux gains importance, as they provide different options for CO₂ mitigation strategies. In this study, a simulation approach combining Markov chain processes and carbon pools for forests and soils has been implemented to study the carbon flows over a period of time. Markov chains have been computed by converting the land use change and forestry data of India from 1997 to 1999 into a matrix of conditional probabilities reflecting the changes from one class at time t to another class time t+1. Results from Markov modeling suggested Indian forests as a potential sink for 0.94 Gt carbon, with an increase in dense forest area of about 75.93 Mha and decrease of about 3.4 Mha and 5.0 Mha in open and scrub forests, if similar land use changes that occurred during 1997–1999 would continue. The limiting probabilities suggested 34.27 percent as dense forest, 6.90 as open forest, 0.4 percent mangrove forest, 0.1 percent scrub and 58 percent as non-forest area. Although Indian forests are found to be a potential carbon sink, analysis of results from transition probabilities for different years till 2050 suggests that, the forests will continue to be a source of about 20.59 MtC to the atmosphere. The implications of these results in the context of increasing anthropogenic pressure on open and scrub forests and their contribution to carbon source from land use change and forestry sector are discussed. Some of the mitigation aspects to reduce greenhouse gas emissions from land use change and forestry sector in India are also reviewed in the study.     

中国农业现代化发展水平的空间非均衡及动态演进

研究中国农业现代化发展水平的地区差距以及内部动态分布特征对于制定农业现代化发展的相关政策具有重要意义。本文利用1997-2013年中国31个省、市(自治区)的面板数据,构建了农业现代化发展水平的指标体系,进而采用熵权综合指数法测算出中国农业现代化发展水平的综合指数,同时借助Dagum基尼系数和Markov链估计方法对中国农业现代化发展水平的地区差距及其分布动态进行了实证研究,得到如下结论:1Dagum基尼系数及分解结果表明,中国农业现代化发展水平的总体差距在样本考察期内呈现出先上升、后下降的演变趋势。从三大地区来看,东部地区内差距呈现出上升趋势,而中部和西部地区内差距则呈现出下降趋势。地区间差距是造成中国农业现代化发展水平空间非均衡的首要原因,并且其对总体差距的贡献率呈波动上升趋势;地区内差距对总体差距的贡献率变化较小,而超变密度对总体差距的贡献率呈现出先上升、再下降的演变趋势。2Markov链分析表明,中国农业现代化发展水平状态流动性较低。从整体来看,中国农业现代化发展水平存在较为明显的上升趋势,并且农业现代化发展的低水平省份将逐步减少,总体向中等以上水平发展。针对本文实证结果,由此得到如下政策建议:首先,加大国家对西部地区农业基础设施建设的投入,引导农业科技人才回归;其次,加强区域间农业现代化发展全方位的交流与合作,发挥省区之间的协同效应;最后,因地制宜,充分利用资源禀赋,推动农业现代化发展。     

BAYESIAN MODELS OF FORECASTED TIME SERIES1

Bayesian Processor of Forecasts (BPF) combines a prior distribution, which describes the natural uncertainty about the realization of a hydrologic process, with a likelihood function, which describes the uncertainty in categorical forecasts of that process, and outputs a posterior distribution of the process, conditional upon the forecasts. The posterior distribution provides a means of incorporating uncertain forecasts into optimal decision models. We present fundamentals of building BPF for time series. They include a general formulation, stochastic independence assumptions and their interpretation, computationally tractable models for forecasts of an independent process and a first-order Markov process, and parametric representations for normal-linear processes. An example is shown of an application to the annual time series of seasonal snowmelt runoff volume forecasts.     

Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA

This paper presents an approach to modeling land-cover change as a function of land-use change. We argue that, in order to model the link between socio-economic change and changes in forest cover in a region that is experiencing residential and recreational development and agricultural abandonment, land-use and land-cover change need to be represented as separate processes. Forest-cover change is represented here using two transition probabilities that were calculated from Landsat imagery and that, taken together, describe a Markov transition matrix between forest and non-forest over a 10-year period. Using a three-date land-use data set, compiled and interpreted from digitized parcel boundaries, and scanned aerial photography for 136 sites (c. 2500 ha) sampled from the Upper Midwest, USA, we test functional relationships between forest-cover transition probabilities, standardized to represent changes over a decade, and land-use conditions and changes within sample sites. Regression models indicated that about 60% of the variation in the average forest-cover transition probabilities (i.e. from forest to non-forest and vice versa) can be predicted using three variables: amount of agricultural land use in a site; amount of developed land use; and the amount of area increasing in development. In further analysis, time lags were evaluated, showing that agricultural abandonment had a relatively strong time-lag effect but development did not. We demonstrate an approach to using forest-cover transition probabilities to develop spatially-constrained simulations of forest-cover change. Because the simulations are based on transition probabilities that are indexed to a particular time and place, the simulations are improved over previous applications of Markov transition models. This modeling approach can be used to predict forest-cover changes as a result of socio-economic change, by linking to models that predict land-use change on the basis of exogenous human-induced drivers.     

青藏铁路列车运行控制系统的安全性分析

分析了青藏铁路中多种不利因素对轨道电路参数的影响 ;提出了在青藏铁路不宜采用现行基于轨道电路的列车运行控制系统 ,而应选择基于通信的列车运行控制系统的理由 ;给出了系统的基本结构及提高系统可靠性的措施 ;与此同时 ,就人们关心的无线传输列车控制数据的可靠性和安全性难题 ,通过建立马尔可夫模型的方法进行了分析     

基于模糊聚类的马氏链模型在交通事故预测中的应用

运用模糊聚类分析方法,借助于MATLAB软件,对中国31个地区交通事故的危害程度进行动态分类和综合评价。根据中国1999—2005年31个地区的交通事故4个指标和5个社会经济因素数据,把31个地区分为3类,即轻灾区、较重灾区、重灾区;其中浙江、福建与宁夏的交通事故危害严重,上海、北京与天津的交通状况正在好转,这与客观实际符合。根据模糊聚类的结果,求得交通事故状态转移概率矩阵,利用马氏链模型可预测未来31个地区的交通事故发展趋势。     

STOCHASTIC MODELING OF MONTHLY AND DAILY RAINFALL SEQUENCES1

ABSTRACT: A monthly model and two daily models (I and II) are presented for the purpose of generating monthly and daily rainfall sequences in the Quae Yai river basin in Thailand. Performance of the models are evaluated by comparing the statistical parameters of the generated sequences with those from historical data. For monthly generation, Thomas-Fiering model worked satisfactorily in spite of the monthly correlations being weak, if any. Daily Model I, which assumes no persistence between daily rainfall amounts within the wet spells, could not preserve some important parameters regardless of the simplicity in model construction. Application of multi-state transition probability matrix model gave good results, although the user has to modify some parameters looking at the performance of the model for each historical record.     

基于灰色-马尔科夫模型的我国能源消费结构优化调整

本文以我国1997—2010年的消费结构数据为基础,运用灰色-马尔科夫链模型对数据进行了分析和预测,然后通过MATLAB预测了我国未来十年的能源消费结构,结合我国政府承诺的非化石能源目标对模型预测结果做了进一步的修正,最后根据能源发展趋势给出了能源消费结构的优化策略。