基于MLP-ANN与Markov Chain的土地利用变化预测方法——以锡林郭勒盟为例

以北方草地典型地区—内蒙古锡林郭勒盟为案例区,在1995年到2000年的土地利用变化与驱动力分析的基础上,利用土地利用转换类型和驱动力模型,采用多层感知人工神经网络模型分析了各种土地利用类型未来的转换潜力;利用马尔可夫链模型,预测了2005和2010年土地利用格局。预测结果显示：高覆盖度草地减少幅度最大,中覆盖度草地减少相对和缓,高、中覆盖度草地的减少造成了未利用地和低覆盖度草地的增加,尤其是前者增加的幅度最大;从空间分布看,高覆盖度草地的减少集中在西北部地区,主要转变为中低覆盖度草地,中覆盖度草地的减少主要集中在西南部地区,其流向主要是以沙化土地为主的未利用地;案例研究表明,多层感知人工神经网络模型与马尔可夫链模型的结合与应用能够在很大程度上预测稳定驱动力作用下的土地利用变化趋势,从而为生态干预提供指导。     

A Bayesian hierarchical model for monitoring harbor seal changes in Prince William Sound, Alaska

Bayesian hierarchical models were used to assess trends of harbor seals, Phoca vitulina richardsi, in Prince William Sound, Alaska, following the 1989 Exxon Valdez oil spill. Data consisted of 4–10 replicate observations per year at 25 sites over 10 years. We had multiple objectives, including estimating the effects of covariates on seal counts, and estimating trend and abundance, both per site and overall. We considered a Bayesian hierarchical model to meet our objectives. The model consists of a Poisson regression model for each site. For each observation the logarithm of the mean of the Poisson distribution was a linear model with the following factors: (1) intercept for each site and year, (2) time of year, (3) time of day, (4) time relative to low tide, and (5) tide height. The intercept for each site was then given a linear trend model for year. As part of the hierarchical model, parameters for each site were given a prior distribution to summarize overall effects. Results showed that at most sites, (1) trend is down; counts decreased yearly, (2) counts decrease throughout August, (3) counts decrease throughout the day, (4) counts are at a maximum very near to low tide, and (5) counts decrease as the height of the low tide increases; however, there was considerable variation among sites. To get overall trend we used a weighted average of the trend at each site, where the weights depended on the overall abundance of a site. Results indicate a 3.3% decrease per year over the time period.     

Modelling Replicated Weed Growth Data using Spatially-varying Growth Curves

Weed growth in agricultural fields constitutes a major deterrent to the growth of crops, often resulting in low productivity and huge losses for the farmers. Therefore, proper understanding of patterns in weed growth is vital to agricultural research. Recent advances in Geographical Information Systems (GIS) now allow geocoding of agricultural data, which enable more sophisticated spatial analysis. Our current application concerns the development of statistical models for conducting spatial analysis of growth patterns in weeds. Our data comes from an experiment conducted in Waseca, Minnesota, that recorded growth of the weed Setariaspp. We capture the spatial variation in Setaria spp. growth using spatially-varying growth curves. An added challenge is that these designs are spatially replicated, with each plot being a lattice of sub-plots. Therefore, spatial variation may exist at different resolutions – a macro level variation between the plots and micro level variation between the sub-plots nested within each plot. We develop a Bayesian hierarchical framework for this setting. Flexible classes of models result which are fitted using simulation-based methods.     

受限空间中CO与水蒸汽阻尼瓦斯爆炸的反应动力学模拟研究

为了探求一氧化碳与水蒸汽参与瓦斯爆炸的化学反应动力学过程的阻尼效应,建立了受限空间中瓦斯爆炸反应的数学模型。数值计算结果表明,结果表明在瓦斯爆炸过程中,瓦斯-空气混合气体含有10%的一氧化碳,虽然会延迟瓦斯爆炸时间,抑制瓦斯爆炸,但是H、O自由基浓度、瓦斯爆炸温度和压力比不加入一氧化碳时升高,同时对CO2、NO的生成起促进作用;当混合气体中含有10%的水蒸汽时,H、O自由基浓度降低,瓦斯爆炸温度和压力也随之降低,致灾性气体CO2、NO的生成得到抑制。虽然一氧化碳对瓦斯爆炸有一定的阻尼效应,但是由于一氧化碳对部分致灾性气体的生成有促进作用,因此,在阻尼瓦斯爆炸方面,水蒸汽的效果要好于一氧化碳。     

Bayesian Nonstationary Frequency Analysis of Hydrological Variables1

Ouarda, T.B.M.J. and S. El‐Adlouni, 2011. Bayesian Nonstationary Frequency Analysis of Hydrological Variables. Journal of the American Water Resources Association (JAWRA) 47(3):496‐505. DOI: 10.1111/j.1752‐1688.2011.00544.x Abstract: The present paper provides a discussion of nonstationary frequency analysis models in hydrology with a focus on the Bayesian approach. The Bayesian model provides an efficient estimation framework of hydrological quantiles in the presence of nonstationarity. In nonstationary frequency analysis models, the parameters are functions of covariates, allowing for dependent parameters and trends. The use of the nonstationary Generalized Maximum Likelihood Estimation method in hydrologic frequency analysis is discussed. This model allows using prior information concerning the variables under study and considering a number of models (linear, quadratic, etc.) of the dependence of the parameters on covariates. A discussion is also provided concerning the use of the reversible jump Monte Carlo Markov Chain procedure which allows carrying out the estimation of the posterior distributions of the parameters and the selection of the Bayesian model at the same time. An application to a case study is presented to illustrate the potential of the model.     

PCR-DGGE研究Carrousel 2000氧化沟工艺调试过程中的微生物多样性

收集Carrousel 2000型氧化沟工艺启动过程中活性污泥样品,直接提取微生物的基因组DNA并纯化,然后对细菌16S rDNA的V3高变区进行PCR扩增和DGGE分离,通过比较DGGE图谱的相似性来研究工艺调试过程中微生物种群的变化情况.研究表明,活性污泥中具有非常丰富的微生物种群.调试初期水质波动对氧化沟中微生物种群的影响非常明显,但接种的成熟活性污泥中微生物种群能够很快适应新型氧化沟工艺的结构及水力特性.调试从4月开始,4月氧化沟中微生物种群相似性Cs最大值为68.9%,5~6月Cs最大值为70.8%,8月Cs最大值为73.0%,可见氧化沟中微生物种群相似性逐渐增加,直至稳定.在此过程中,系统对COD、氨氮的处理效果同步提高并趋于稳定.综合分析好氧活性污泥在氧化沟中驯化期为2个月.图4表4参12     

Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo

Coral reefs are threatened ecosystems, so it is important to have predictive models of their dynamics. Most current models of coral reefs fall into two categories. The first is simple heuristic models which provide an abstract understanding of the possible behaviour of reefs in general, but do not describe real reefs. The second is complex simulations whose parameters are obtained from a range of sources such as literature estimates. We cannot estimate the parameters of these models from a single data set, and we have little idea of the uncertainty in their predictions.We have developed a compromise between these two extremes, which is complex enough to describe real reef data, but simple enough that we can estimate parameters for a specific reef from a time series. In previous work, we fitted this model to a long-term data set from Heron Island, Australia, using maximum likelihood methods. To evaluate predictions from this model, we need estimates of the uncertainty in our parameters. Here, we obtain such estimates using Bayesian Metropolis-Coupled Markov Chain Monte Carlo. We do this for versions of the model in which corals are aggregated into a single state variable (the three-state model), and in which corals are separated into four state variables (the six-state model), in order to determine the appropriate level of aggregation. We also estimate the posterior distribution of predicted trajectories in each case.In both cases, the fitted trajectories were close to the observed data, but we had doubts about the biological plausibility of some parameter estimates. We suggest that informative prior distributions incorporating expert knowledge may resolve this problem. In the six-state model, the posterior distribution of state frequencies after 40 years contained two divergent community types, one dominated by free space and soft corals, and one dominated by acroporid, pocilloporid, and massive corals. The three-state model predicts only a single community type. We conclude that the three-state model hides too much biological heterogeneity, but we need more data if we are to obtain reliable predictions from the six-state model. It is likely that there will be similarly large, but currently unevaluated, uncertainty in the predictions of other coral reef models, many of which are much more complex and harder to fit to real data.     

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.     

Using SFA indicators to support environmental policy

In order to improve the link from Substance Flow Analysis (SFA) studies to environmental policy, a translation is made from the SFA overview of flows and stocks into a limited set of indicators. This set is designed to evaluate a region’s substance regime with regard to environmental quality and sustainable development, including problem shifting in time and space.     

链条式刮油刮泥机在治理含油污水中的应用

在平流隔油池安装山东省新泰市石油化工机械厂生产的ＧＴ－１型链条式刮油刮泥机，操作简单，运行稳定，收油效果良好，除油效率由以前的７３．４９％提高到８３．０６％，出水含油可控制在５０ｍｇ／Ｌ以下，每年增加经济效益１３．６万元