用Markov模型预测长江水质

由于长江水质的污染程度日益严重,为了说明治理长江对长江水质进行了简单的评价,保护长江迫在眉睫,首先,根据长江流域的17个观测站近两年多的水质检测数据统计,说明了近两年多来长江的防污治理工作有一定的效果。然后,根据1995~2004年长江流域水质的数据报告,考虑各类水之间的相互转化,构造了马尔柯夫(Markov)转移矩阵,建立了马尔柯夫预测模型,通过已有的观测数据验证了该模型的正确性及有效性。运用该马尔柯夫模型预测未来10年水质的变化趋势,即Ⅰ、Ⅱ、Ⅲ、Ⅳ类水逐年减少,而Ⅴ、劣Ⅴ类水逐年增加。到2014年,长江的第Ⅰ类水只有0.4 059%,劣Ⅴ类水达到26.2 714%,不可饮用水（即第Ⅳ,Ⅴ,劣Ⅴ类水）将达到47.468%,为此应采取更加有效的治理措施,控制长江水质的恶化。最后,通过计算,得到了每年需要处理污水的最小百分比,才能杜绝劣Ⅴ类水,将第Ⅳ、Ⅴ类水控制在20%内,从而才能保证我们有足够的饮用水     

Vegetation restoration dynamics in the forest-steppe system of the middle Volga region

The current dynamics of ecosystems of the forest-steppe system is most adequately described via determining the probable trajectories of changes in its elements in a simple Markov chain. The obtained data have made it possible to determine the direction of succession and draw its scheme. Theoretically possible series of community development are presented. The succession of community restoration from the spread of shrubs over steppificated fallow lands to the formation of low forests of European bird cherry has been analyzed. The restoration cycle of ecosystem dynamics is described by eight age stages.     

A SCALE‐INVARIANT GAUSS‐MARKOV MODEL FOR DESIGN STORM HYETOGRAPHS1

Abstract. Hyetographs are essential to many hydrological designs. Many studies have shown that hyetographs are specific to storm types and durations. Recent work presented evidence that dimensionless hyetographs are scale invariant. We show that the simple scaling property of rainfall guarantees that the normalized rainfall rates of different storm durations are identically distributed and propose a nonstationary Gauss‐Markov model based on the annual maximum events that arise from the dominant storm type. We derive the unique estimators for the parameters of the Gauss‐Markov model under two constraints that: (a) the typical peak rainfall rate is preserved, and (b) the most likely hyetograph is obtained. One attractive feature of this model is that it allows translating hyetographs between storms of different durations. Two examples illustrate our model.     

晋陕蒙接壤区典型生态过渡带景观变化遥感研究

晋陕蒙接壤区是典型的生态过渡带,生态环境具有波动性、脆弱性和敏感性.笔者采用2个时期遥感影像作为基本信息源,在GIS的支持下,建立空间信息库,选取斑块大小、分维数、形状指数、景观破碎度和景观优势度等指标,对景观空间格局的动态变化进行了分析.同时利用马尔柯夫模型对该区的景观要素转换进行了模拟.结果显示,1988～2000年期间,该区的景观格局发生了显著变化,景观趋于破碎化.      

A note on data augmentation and iterative simulation for survival rate estimation

In this paper we examine the use of data augmentation techniques for simplifying iterative simulation in the context of both Bayesian and classical statistical inference for survival rate estimation. We examine two distinct model families common in population ecology to illustrate our ideas, ring-recovery models and capture–recapture models, and we present the computational advantage of this approach. We discuss also the fact that problems associated with identifiability in the classical framework can be overcome using data augmentation, but highlight the dangers in doing so under both inferential paradigms.

基于人工免疫算法的离散隐马尔科夫故障诊断模型优化

目的解决离散隐马尔科夫模型在行星齿轮箱故障诊断中的自适应性和泛化性问题。方法建立人工免疫优化模型,将包含易被误判样本的多样本集作为抗原,以其正确识别率为适应度函数,不断对初始观测矩阵进行增殖、变异和选择,获得识别率最高时的初始观测矩阵,利用人工免疫算法对隐马尔科夫故障诊断模型的初始观测矩阵进行优化。结果将该方法应用于行星齿轮箱的故障诊断中,通过不同工况下的对比试验、单样本和多样本优化对比试验,验证了优化后的隐马尔科夫故障诊断模型的诊断效果。结论优化后的隐马尔科夫故障诊断模型具有更好的适应性,诊断精度显著提高。     

Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model

Nitrous oxide (N₂O) is the main biogenic greenhouse gas contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate therefore requires a capacity to predict N₂O emissions in relation to environmental conditions and crop management. Biophysical models simulating the dynamics of carbon and nitrogen in agro-ecosystems have a unique potential to explore these relationships, but are fraught with high uncertainties in their parameters due to their variations over time and space. Here, we used a Bayesian approach to calibrate the parameters of the N₂O submodel of the agro-ecosystem model CERES-EGC. The submodel simulates N₂O emissions from the nitrification and denitrification processes, which are modelled as the product of a potential rate with three dimensionless factors related to soil water content, nitrogen content and temperature. These equations involve a total set of 15 parameters, four of which are site-specific and should be measured on site, while the other 11 are considered global, i.e. invariant over time and space. We first gathered prior information on the model parameters based on the literature review, and assigned them uniform probability distributions. A Bayesian method based on the Metropolis–Hastings algorithm was subsequently developed to update the parameter distributions against a database of seven different field-sites in France. Three parallel Markov chains were run to ensure a convergence of the algorithm. This site-specific calibration significantly reduced the spread in parameter distribution, and the uncertainty in the N₂O simulations. The model’s root mean square error (RMSE) was also abated by 73% across the field sites compared to the prior parameterization. The Bayesian calibration was subsequently applied simultaneously to all data sets, to obtain better global estimates for the parameters initially deemed universal. This made it possible to reduce the RMSE by 33% on average, compared to the uncalibrated model. These global parameter values may be used to obtain more realistic estimates of N₂O emissions from arable soils at regional or continental scales.     

长株潭城市群地类转移的碳传导效应与预测

土地利用碳排放是影响城市碳达峰、碳中和实现的重要因素.基于土地利用遥感数据和碳排放估算模型,得到长株潭城市群的土地利用碳排放量,借助转移矩阵分析了长株潭城市群土地利用转移的碳传导效应.此外,采用马尔科夫模型预测2030年和2060年的长株潭土地利用碳排放量.结果表明：①1995~2018年长株潭城市群土地利用净碳排放从810.84×10⁴ t增加到2015.41×10⁴ t,碳源/汇比整体呈上升趋势.其中,建设用地是主要的碳排放源,林草地是主要的碳汇.②不同时段地类转移引致的碳传导最终均表现为净碳排放,在时间上呈现先增加后减少的态势.其中以林地和耕地向建设用地转移产生的碳传导最为显著,涉及草地、水域和未利用地的碳传导效应微弱.③预测结果表明,长株潭城市群的土地利用碳排放预测量处于持续上涨态势,如若仍按目前趋势发展,则如期实现"双碳目标"存在难度.政府需要在加强林地的碳吸收能力以提升生态系统碳汇增量和遏制建设用地的无序扩张以减少碳源两方面着力,加快长株潭城市群的绿色低碳建设.上述结果为长株潭城市群开展低碳导向的城市土地利用调控提供了重要参考.     

用马尔可夫链模型预测宁南山区旱情

宁南山区干旱频繁,严重影响农业生产。根据固原气象站35年(1957～1991)雨量资料,应用马尔可失链模型预测了该区1992～1996年的雨量与旱情趋势。结果表明,今后五年将出现三年春夏旱、二年正常,秋季有四年正常、一年多雨;预测的1992年4～6月和6～9月的干旱等级值与实际完全相符。该模型预测结果有较高可信度,对该区农业生产有参考价值。     

基于BP神经网络与马尔可夫链的污水处理厂脱氮效果模拟预测

在实际污水处理厂运行过程中,其最终出水水质会受多种因素影响制约,而基于生物反应机理的活性污泥数学模型(ASM)并未将这些生物反应以外的因素考虑在内,由此带来一些不足.对此,本文提出可通过基于数据挖掘技术的黑箱模型对污水厂处理效果进行模拟预测.结合具体实际分析,提出可将BP神经网络与马尔可夫链组合应用于污水处理脱氮效果预测中.首先,通过BP神经网络模型对北京某大型污水处理厂实际进出水数据和工艺参数进行粗略拟合;其次,利用马尔可夫链对拟合结果及误差进行状态划分以进一步提高预测精确度;最后,运用基于BP神经网络与马尔可夫链的组合模型预测分析了该厂的实际出水水质.试验结果表明,BP神经网络适用于污水处理脱氮过程的拟合计算,而通过与马尔可夫链组合,可以提高模拟预测的精度和可靠性.