大气环境数据分析预测方法对比研究

以西安市2006年9月27日至2008年5月3日每日的SO2平均浓度时间序列为例,应用时间序列分析对前555个数据进行拟合,得到合适的时间序列模型ARIMA(1,1,2);利用神经网络中的BP神经网络和RBF神经网络对同样的样本进行训练,用这三种方法对2008年4月4日至2008年5月3日的SO2日均浓度值进行了预测,并用同样的方法分析预测了同期PM10日均浓度值,最后比较了它们的预测效果。结果表明,利用这三种方法进行浓度预测都是可行的,其中RBF神经网络法的预测误差最小,效果最好。     

Fault detection and diagnostics of a three-phase separator

A high demand of oil products on daily basis requires oil processing plants to work with maximum efficiency. Oil, water and gas separation in a three-phase separator is one of the first operations that are performed after crude oil is extracted from an oil well. Failure of the components of the separator introduces the potential hazard of flammable materials being released into the environment. This can escalate to a fire or explosion. Such failures can also cause downtime for the oil processing plant since the separation process is essential to oil production. Fault detection and diagnostics techniques used in the oil and gas industry are typically threshold based alarm techniques. Observing the sensor readings solely allows only a late detection of faults on the separator which is a big deficiency of such a technique, since it causes the oil and gas processing plants to shut down.A fault detection and diagnostics methodology for three-phase separators based on Bayesian Belief Networks (BBN) is presented in this paper. The BBN models the propagation of oil, water and gas through the different sections of the separator and the interactions between component failure modes and process variables, such as level or flow monitored by sensors installed on the separator. The paper will report on the results of the study, when the BBNs are used to detect single and multiple failures, using sensor readings from a simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the separator.     

Hybrid governance tensions fuelling self-reflexivity in Alternative Food Networks: the case of the Brussels GASAP (solidarity purchasing groups for peasant agriculture)

This paper applies the concept of Hybrid Governance to the analysis of the GASAP (Groupe d’Achats Solidaires de l’Agriculture Paysanne), a solidarity-based producers–consumers network established in Brussels in 2006. The Hybrid Governance concept allows to capture the role of key governance tensions in driving the self-organisation, scaling out and self-reflexive dynamics of Alternative Food Networks (AFNs). The approach provides a multifaceted and sound socio-political account of the ways AFNs arise, self-organise, associate and build networks in the pursuit of their food allocation objectives, often facing a contradictory socio-institutional environment. Three types of governance tensions, i.e. organisational, resource, and institutional, as well as the interactions among them, are analysed throughout the life-course of the GASAP network. The analysis identifies three phases in the GASAP’s life-course, showing how governance tensions and their interrelations arise and play a critical role in conditioning the overall development of the organisation through time. The paper concludes with highlighting prospective values of the hybrid governance approach for the analysis of AFNs in general. These values relate to: the role of the hybrid approach in illuminating on key drivers behind the scaling out of AFNs; the hybrid governance as a tool to conceptualise and sustain the self-reflexive capacity of local food initiatives and the ways by which this approach unravels challenges to build cooperative alliances and networks among a diversity of agents in the food arena.     

The role of science-policy interface in sustainable urban water transitions: Lessons from Rotterdam

This paper investigates the role of the science-policy interface in leveraging transitions to sustainable urban water management. The paper presents a case study of the Dutch city of Rotterdam, which is increasingly regarded as a global leader in adaptive and resilient urban water management. The analysis reveals that Rotterdam’s transition has occurred incrementally over the past 15 years, driven by policy entrepreneurs: largely municipal policy makers and policy practitioners. Strategic use of the science-policy interface (SPI) has facilitated the development of innovative solutions to achieve policy goals and created the enabling conditions necessary for transformative change. The Rotterdam case suggests that an effective SPI requires: (1) compelling water narrative; (2) cross-sectoral collaboration; (3) co-production of knowledge; (4) experiential evidence-based learning; (5) strategic use of trusted scientists; (6) fostering networks; and (7) generating business from science-based innovation. Rotterdam’s strategic approach to knowledge and innovation coupled with a new narrative around water sets it apart from many other cities and adds a new dimension to debates regarding enabling factors for advancing sustainable practices. These findings will be of interest to those engaged in urban water management policy and practice, environmental governance, and debates over transitions more broadly.     

Planning rural water services in Nicaragua: A systems-based analysis of impact factors using graphical modeling

The success or failure of rural water services in the developing world is a result of numerous factors that interact in a complex set of connections that are difficult to separate and identify. This research effort presented a novel means to empirically reveal the systemic interactions of factors that influence rural water service sustainability in the municipalities of Darío and Terrabona, Nicaragua. To accomplish this, the study employed graphical modeling to build and analyze factor networks. Influential factors were first identified by qualitatively and quantitatively analyzing transcribed interviews from community water committee members. Factor influences were then inferred by graphical modeling to create factor network diagrams that revealed the direct and indirect interaction of factors. Finally, network analysis measures were used to identify “impact factors” based on their relative influence within each factor network. Findings from this study elucidated the systematic nature of such factor interactions in both Darío and Terrabona, and highlighted key areas for programmatic impact on water service sustainability for both municipalities. Specifically, in Darío, the impact areas related to the current importance of water service management by community water committees, while in Terrabona, the impact areas related to the current importance of finances, viable water sources, and community capacity building by external support. Overall, this study presents a rigorous and useful means to identify impact factors as a way to facilitate the thoughtful planning and evaluation of sustainable rural water services in Nicaragua and beyond.     

Resource allocation in two species systems: Is it worth acknowledging species interactions?

It is becoming increasingly popular to consider species interactions when managing ecological foodwebs. Such an approach is useful in determining how management can affect multiple species, with either beneficial or detrimental consequences. Identifying such actions is particularly valuable in the context of conservation decision making as funding is severely limited. This paper outlines a new approach that simplifies the resource allocation problem in a two species system for a range of species interactions: independent, mutualism, predator-prey, and competitive exclusion. We assume that both species are endangered and we do not account for decisions over time. We find that optimal funding allocation is to the conservation of the species with the highest marginal gain in expected probability of survival and that, across all except mutualist interaction types, optimal conservation funding allocation differs between species. Loss in efficiency from ignoring species interactions was most severe in predator-prey systems. The funding problem we address, where an ecosystem includes multiple threatened species, will only become more commonplace as increasing numbers of species worldwide become threatened.     

Collaborative networks and new ways of knowing

To move beyond legal and regulatory gridlock around water issues in the California Bay-Delta, a new inter-agency initiative, commonly known as CALFED, was created in 1994. CALFED has been an ongoing experiment in policy innovation. Part of the change in management practice has involved constructing new arenas that engage multiple perspectives and transform regulatory impasse into provisional steps forward. We examine the construction of so-called boundary objects, which are forums and policy instruments that cross group boundaries and foster integrative deliberation. We compare the design and action of two boundary objects created by CALFED, namely the Environmental Water Account (EWA) and the Water Use Efficiency (WUE) program. We find that the presence of the boundary object, in itself, does little to explain the success of each policy experiment. Rather, the answer lies in the types of network interactions that result, along with the way meaning is coproduced. In fact, rather than create new patterns of interrelationship (e.g., between fish habitat advocates and pump station operators), the boundary object might further embed institutionalized routines. To more deeply understand what makes the new institution an integrative one, we introduce the concept of Ways of Knowing which explains how new knowledge emerges from the network of new relationships.     

Determining the community structure of the coral Seriatopora hystrix from hydrodynamic and genetic networks

The exchange of genetic information between coral reefs through the transport of larvae can be described in terms of networks that capture the linkages between distant populations. A key question arising from these networks is the determination of the highly connected modules (communities). Communities can be defined using genetic similarity or distance statistics between multiple samples but due to limited specimen sampling capacity the boundaries of the communities for the known coral reefs in the seascape remain unresolved. In this study we use the microsatellite composition of individual corals to compare sample populations using a genetic dissimilarity measure (F_ST) which is then used to create a complex network. This network involved sampling 1025 colonies from 22 collection sites and examining 10 microsatellites loci. The links between each sampling site were given a strength that was created from the pair wise F_ST values. The result is an undirected weighted network describing the genetic dissimilarity between each sampled population. From this network we then determined the community structure using a leading eigenvector algorithm within graph theory. However, given the relatively limited sampling conducted, the representation of the regional genetic structure was incomplete. To assist with defining the boundaries of the genetically based communities we also integrated the communities derived from a hydrodynamic and distance based networks. The hydrodynamic network, though more comprehensive, was of smaller spatial extent than our genetic sampling. A Bayesian Belief network was developed to integrate the overlapping communities. The results indicate the genetic population structure of the Great Barrier Reef and provide guidance on where future genetic sampling should take place to complete the genetic diversity mapping.     

基于多元统计分析和RBFNNs的水质评价方法

提出了一种基于多元统计分析和RBFNNs的水质评价方法,其可适用于大尺度、多断面、长时间的水质评价工作,旨在补充以往相关研究工作.其主要程序为:利用方差分析对各断面多年水质监测样本进行时间与空间尺度上的显著差异性分析,识别出具有显著差异的样本,然后通过层次聚类分析把上述样本进行聚类分组,最后应用径向基神经网络对各组样本进行水质评价,并把此评价结果再分解到各断面各时段,此方法的特点为在不损失信息的前提下能大大减轻水质评价工作量,且客观可信、分辨率高,并能综合反映总体与个别特征.以辽宁省太子河为例,通过方差分析将2001～2003年6个断面的144个样本归纳为存在显著差异的74个样本,再通过聚类分析得到9个相似组,相应的水质评价结果为2.7394、4.4306、4.0994、2.777、4.2192、4.1214、4.4129、4.4259、4.4359,与传统单项指数法的结果基本一致.具体落实到各断面,结果表明,2001～2003 年间太子河大部分断面的水质处于Ⅳ类以上.     

Network-based exploration and visualisation of ecological data

Networks – structured graphs consisting of sets of nodes connected by edges – provide a rich framework for data visualisation and exploratory analyses. Although rarely used for the visualisation of ecological data, networks are well suited to this purpose, including data that one might not normally think of as a network. We present a simple method for transforming a data matrix into network format, and show how this can be used as the basis for interactive exploratory analyses of ecological data.The method is demonstrated using a database of marine zooplankton samples acquired in the Southern Ocean. The network analyses revealed zooplankton community structures that are in good agreement with previously published results. Variations in community structure were observed to be related to the temporal and spatial pattern of sampling, as well as to physical environmental factors such as sea ice cover. The analyses also revealed a number of errors in the data, including taxon identification errors and instrument failures.The method allows the analyst to generate networks from different combinations of variables in the data set, and to examine the effects of varying parameters such as the scales of spatial, temporal, and taxonomic aggregation. This flexibility allows the analyst to rapidly gain a number of perspectives on the data and provides a powerful mechanism for exploration.