Physical and Chemical Connectivity of Streams and Riparian Wetlands to Downstream Waters: A Synthesis

Streams, riparian areas, floodplains, alluvial aquifers, and downstream waters (e.g., large rivers, lakes, and oceans) are interconnected by longitudinal, lateral, and vertical fluxes of water, other materials, and energy. Collectively, these interconnected waters are called fluvial hydrosystems. Physical and chemical connectivity within fluvial hydrosystems is created by the transport of nonliving materials (e.g., water, sediment, nutrients, and contaminants) which either do or do not chemically change (chemical and physical connections, respectively). A substantial body of evidence unequivocally demonstrates physical and chemical connectivity between streams and riparian wetlands and downstream waters. Streams and riparian wetlands are structurally connected to downstream waters through the network of continuous channels and floodplain form that make these systems physically contiguous, and the very existence of these structures provides strong geomorphologic evidence for connectivity. Functional connections between streams and riparian wetlands and their downstream waters vary geographically and over time, based on proximity, relative size, environmental setting, material disparity, and intervening units. Because of the complexity and dynamic nature of connections among fluvial hydrosystem units, a complete accounting of the physical and chemical connections and their consequences to downstream waters should aggregate over multiple years to decades.     

A Power Law Model for River Flow Velocity in Iowa Basins

This study explores power law relationships to estimate water flow velocity as a function of discharge and drainage area across river networks. We test the model using empirical data from 214 United States (U.S.) Geological Survey gauging stations distributed over the state of Iowa in the U.S. The empirical data are the measurements of the mean cross‐sectional velocity and concurrent discharge. The data are used to estimate parameters for a state‐wide model and to test for spatial variability for 15 large river basins contained within the state. Spatial differences among the basins are small but some parameters significantly differ from the state‐wide model. Using individual station data, the authors also explore a simpler power law model that disregards dependence on the drainage area. Overall, the study shows that including drainage area improves the model. Our study provides parameter values that can be directly incorporated into a regional scale routing model, and provides a framework for developing flow velocity models for hydraulically similar rivers in the U.S. and the world.     

The ‘Bayesics’ of Ranked Set Sampling

Ranked set sampling can be useful when measurements are expensive but units from the popu- lation can be easily ranked. In this situation one may draw k units from the population, rank them, select one on which to make the expensive measurement, draw another k units, rank them, select one, and so on. The method was originally suggested by McIntyre (1952) in connection with pasture yields and is obviously applicable in other situations as well. Dell and Clutter (1972) and Patil et al. (1994) explain the basics from a classical point of view. Our aim is to examine the procedure from a Bayesian point of view, determine whether ranked set sampling provides advantages over simple random sampling and explore some optimality questions     

车装焊复合加工装备的服役可靠性评估

目的评估某型航天用车装焊复合加工装备在服役过程中的服役可靠性。方法首先分析装备常见的故障模式,统计装备在使用过程中的故障数据,由于装备故障数据样本量较少,所以提出基于贝叶斯理论的小样本车装焊复合加工装备可靠性评估方法,其中通过马尔科夫链蒙特卡罗(MCMC)法抽样解决贝叶斯理论中后验积分复杂的问题。结果确定了车装焊复合加工装备的寿命威布尔分布模型,并运用贝叶斯方法,计算出该车装焊复合加工装备的平均无故障工作时间(MTBF)。结论评估结果略低于设计要求,原因是目前该装备处于服役初期,服役初期故障数据较多。待进入稳定服役期时,故障率会有一定程度的降低后趋于稳定,MTBF会有一定幅度的增加,所以该车装焊复合加工装备MTBF基本满足设计要求。     

Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird

We assessed the occurrence of a common river bird, the Plumbeous Redstart Rhyacornis fuliginosus, along 180 independent streams in the Indian and Nepali Himalaya. We then compared the performance of multiple discrimant analysis (MDA), logistic regression (LR) and artificial neural networks (ANN) in predicting this species’ presence or absence from 32 variables describing stream altitude, slope, habitat structure, chemistry and invertebrate abundance. Using the entire data (=training set) and a threshold for accepting presence in ANN and LR set to P≥0.5, ANN correctly classified marginally more cases (88%) than either LR (83%) or MDA (84%). Model performance was assessed from two methods of data partitioning. In a ‘leave-one-out’ approach, LR correctly predicted more cases (82%) than MDA (73%) or ANN (69%). However, in a holdout procedure, all the methods performed similarly (73–75%). All methods predicted true absence (i.e. specificity in holdout: 81–85%) better than true presence (i.e. sensitivity: 57–60%). These effects reflect species’ prevalence (=frequency of occurrence), but are seldom considered in distribution modelling. Despite occurring at only 36% of the sites, Plumbeous Redstarts are one of the most common Himalayan river birds, and problems will be greater with less common species. Both LR and ANN require an arbitrary threshold probability (often P=0.5) at which to accept species presence from model prediction. Simulations involving varied prevalence revealed that LR was particularly sensitive to threshold effects. ROC plots (received operating characteristic) were therefore used to compare model performance on test data at a range of thresholds; LR always outperformed ANN. This case study supports the need to test species’ distribution models with independent data, and to use a range of criteria in assessing model performance. ANN do not yet have major advantages over conventional multivariate methods for assessing bird distributions. LR and MDA were both more efficient in the use of computer time than ANN, and also more straightforward in providing testable hypotheses about environmental effects on occurrence. However, LR was apparently subject to chance significant effects from explanatory variables, emphasising the well-known risks of models based purely on correlative data.     

Developing an empirical model of phytoplankton primary production: a neural network case study

We describe the development of a neural network model for estimating primary production of phytoplankton. Data from an enriched estuary in the eastern United States, Chesapeake Bay, were used to train, validate and test the model. Two error backpropagation multilayer perceptrons were trained: a simpler one (3-5-1) and a more complex one (12-5-1). Both neural networks outperformed conventional empirical models, even though only the latter, which exploits a larger suite of predictive variables, provided truly accurate outputs. The application of this neural network model is thoroughly discussed and the results of a sensitivity analysis are also presented.     

人工神经网络和专家系统在污水生物处理系统中的应用

对近年来国内外污水生物处理系统中人工神经网络和专家系统的应用进行了简要的回顾。分析了废水生物处理工艺难于控制的原因及人工神经网络和专家系统的结构和特点。结果表明．国外智能控制发展迅速,并且应用领域遍及污水生物处理的各个方面,国内尚处于起步阶段。简要探讨了废水生物处理智能控制今后应深入研究的问题及方向。     

基于BN的FTA在通用航空风险评价中的应用

针对事故树分析法(FTA)在风险评价中的局限性,采用以事故树为基础建立的贝叶斯网络(BN)风险模型,对通用航空中的两机空中相撞事故进行分析和推理,对事故模型进行改进和修正时,注重基事件的多态性和事件间的逻辑合理性。根据贝叶斯推理得出的数据,找到了事故的主要致因。结果表明,基于BN的FTA既能向前预测顶事件的发生概率,又能向后诊断基本事件的后验概率,可以更好地对通用航空风险进行评价。     

基于复杂网络的城市燃气输配系统失效因素分析

将复杂网络相关理论方法引入城市燃气输配系统失效因素的分析中。选取引起城市燃气输配系统失效的主要影响因素为节点,以它们之间的相互关系为边,建立网络并进行相关分析。发现节点的度分布近似具有幂律的特性,幂指数为1.76;通过节点度的分析,确定出管道附属设备、用户设施失效、第三方影响破坏等因素为引起输配系统失效的主要原因,与引起燃气事故的统计分析资料基本相符合。这为确定城市燃气输配系统关键失效因素提供了一种新的思路。     

矿井通风网络数据可靠性检验

进行矿井通风阻力测定时,由于受环境的温度、湿度、压力传递存在时间差以及测点处标高不准确等因素的影响,根据实测数据,计算所得矿井巷道基础数据存在一定的误差,常用的测量数据检验方法只能对若干条通路进行闭合回路检验,而不能对全矿井通风网络基础参数进行可靠性检验。本文中所介绍的检验方法将风网基础数据输入通风网络解算软件,以实测风量和经过计算所得巷道风阻为基准,实测风量为目标条件,进行计算机通风网络模拟,对全矿井巷道风阻进行连续的优化调整,使通风网络模拟结果与实际测定各巷道风量基本符合,从而实现全矿井风网基础数据的可靠性检验。该方法在某矿进行了实际应用,实践证明此方法科学、准确的获得了全矿巷道风阻参数,为矿井的通风改造奠定了技术基础。