A new synergetic paradigm in environmental numerical modeling: Hybrid models combining deterministic and machine learning components

A new type of environmental numerical models, hybrid environmental numerical models (HEMs) based on combining deterministic modeling and machine learning components, is introduced and formulated. Conceptual and practical possibilities of developing HEM, as an optimal synergetic combination of the traditional deterministic/first principles modeling (like that used for solving PDEs on the sphere representing model dynamics of global climate models) and machine learning components (like accurate and fast neural network emulations of model physics or chemistry processes), are discussed. Examples of developed HEMs (hybrid climate models and a hybrid wind–wave ocean model) illustrate the feasibility and efficiency of the new approach for modeling extremely complex multidimensional systems.     

Social network theory in the behavioural sciences: potential applications

Social network theory has made major contributions to our understanding of human social organisation but has found relatively little application in the field of animal behaviour. In this review, we identify several broad research areas where the networks approach could greatly enhance our understanding of social patterns and processes in animals. The network theory provides a quantitative framework that can be used to characterise social structure both at the level of the individual and the population. These novel quantitative variables may provide a new tool in addressing key questions in behavioural ecology particularly in relation to the evolution of social organisation and the impact of social structure on evolutionary processes. For example, network measures could be used to compare social networks of different species or populations making full use of the comparative approach. However, the networks approach can in principle go beyond identifying structural patterns and also can help with the understanding of processes within animal populations such as disease transmission and information transfer. Finally, understanding the pattern of interactions in the network (i.e. who is connected to whom) can also shed some light on the evolution of behavioural strategies.     

Spatial distribution of main forest soil groups in Croatia as a function of basic pedogenetic factors

The model of spatial distribution of main forest soil groups in Croatia was developed as a function of basic pedogenetic factors: lithological substratum, macroclimate and relief. Used data about soil group, lithological substratum, terrain slope and aspect were collected on 1881 soil profiles. Macroclimatic data were estimated for each soil profile by spatial interpolation between meteorological stations. Feedforward neural networks were used as modelling tool. The final model has total classification correctness of 63.5% for training data set and 62.3% for independent test data set. The best result (86.4%) was achieved for fluvisols which are strongly spatially correlated with alluvial sediment in a flood plains. The worst result was achieved for luvisol (14.2%) which mainly comprised very old soils, probably developed under pedogenetic factors different from actual. The model was applied on entire Croatian territory aiming at construction of potential spatial distribution of main forest soils (without human impact), which was compared by the potential spatial distribution of major forest types modelled independently.     

Ecological Networks as Conceptual Frameworks or Operational Tools in Conservation

Abstract:  The establishment of ecological networks (ENs) has been proposed as an ideal way to counteract the increasing fragmentation of natural ecosystems and as a necessary complement to the establishment of protected areas for biodiversity conservation. This conservation tool, which comprises core areas, corridors, and buffer areas, has attracted the attention of several national and European institutions. It is thought that ENs can connect habitat patches and thus enable species to move across unsuitable areas. In Europe, however, ENs are proposed as an oversimplification of complex ecological concepts, and we maintain that they are of limited use for biodiversity conservation for several reasons. The ENs are species specific and operate on species-dependent scales. In addition, the information needed for their implementation is only available for a handful of species. To overcome these limitations, ENs have been proposed on a landscape scale (and for selected "focal" species), but there is no indication that the structural composition of core areas, corridors, and buffer areas could ensure the functional connectivity and improve the viability of more than a few species. The theory behind ENs fails to provide sufficient practical information on how to build them (e.g., width, shape, structure, content). In fact, no EN so far has been validated in practice (ensuring connectivity and increasing overall biodiversity conservation), and there are no signs that validation will be possible in the near future. In view of these limitations, it is difficult to justify spending economic and political resources on building systems that are at best working hypotheses that cannot be evaluated on a practical level.     

基于实时控制的智能控制在废水处理中的应用

针对废水处理系统具有时变性、非线性、复杂性和不确定性等特点,提出采用智能控制对其进行实时有效控制从而提高处理效率、降低能耗,对智能控制的主要分支模糊控制、神经网络控制和专家系统进行讨论,介绍总结了国内外专家学者应用智能控制技术实现水处理中实时控制的最新动态。结果表明,废水处理系统采用智能控制可以有效地实现实时控制,提高处理效率、降低能耗和成本。     

Personality and social networks in organizations: A review and future directions

Recent research linking individuals' personality characteristics to their social networks has brought a new understanding of how individual patterns of behavior affect networks in organizations. This review summarizes the major advancements in the three areas of social network research relevant to organizational behavior: (a) brokerage and structural holes; (b) network centrality and network size; and (c) strength of ties. This review also provides an agenda outlining three key opportunities for future research. These opportunities involve personality and social network change, bidirectional and dyadic processes, and the potential effect of network position on personality expression. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

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.     

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。