Non-parametric Bayesian Network Research Articles

Waterbird Species Are Highly Sensitive to Wetland Traits: Simulation-Based Conservation Strategies for the Birds of the Sicilian Wetlands (Italy).

In this study, we (a) formulated a general hypothesis about how wetland (functional and structural) traits influence avian diversity, (b) turned this hypothesis into a non-parametric Bayesian network, (c) disentangled the direct and indirect effects of the variables influencing waterbird species, and (d) simulated the changes expected to the levels of avian diversity as a result of numerous counterfactual and management scenarios. We applied our framework to the Sicilian wetlands as a whole; then, we downscaled simulations locally to a wetland of particular interest (Pantano Bruno). We found that (1) waterbird species are highly sensitive to wetland traits; (2) wetland traits have both direct and indirect effects upon alpha avian diversity; (3) the direct and indirect effects of wetland traits can be contrasting; (4) water level fluctuations (benefit), diversions (cost), and salinity (cost) are key factors for waterbird conservation; (5) these wetlands have the potential for hosting a level of alpha avian diversity that is double the baseline (from 19 to 38 species); (6) these wetlands are prone to ecological collapse if all traits deteriorate (from 19 to 6 species per wetland); and (7) the ecological information gained at the regional scale can be properly downscaled to the local scale to make inferences on single wetlands.

Matlatzinca: A PyBANSHEE-based graphical user interface for elicitation of non-parametric Bayesian networks from experts

This article describes the development of a GUI that addresses the challenge of eliciting dependencies between uncertain quantities elicited by experts. While software for eliciting univariate uncertainties is widely available, the mathematical complexity of multivariate dependence models makes direct elicitation difficult. To overcome this, we developed Matlatzinca,11Matlatzinca may be translated from Nahuatl (the language of the Aztecs) to English as “The people that make nets”. This is the name that the Aztecs gave to the inhabitants of the Valley of Toluca in central Mexico who were well known fishermen at the time. Our first release of a GUI for PyBANSHEE is meant for people that wish to quantify (make) Non-parametric Bayesian Networks (Nets) with expert judgments. Hence we name our GUI Matlatzinca. a GUI built on top of the Python module PyBANSHEE. The GUI facilitates the elicitation process and allows experts to model dependencies using a non-parametric Bayesian network without the need for ad hoc programming. A recent practical application shows that the developed GUI is a useful tool for performing dependence elicitations, highlighting the significance of the program for dependence assessment with expert judgment.

Nonparametric Bayesian Networks as a Tool of Multiscale Time Series Analysis and Remote Sensing Data Integration

Introduction. Nonparametric Bayesian networks are a promising tool for analyzing, visualizing, interpreting and predicting the structural and dynamic characteristics of complex systems. Modern interdisciplinary research involves the complex processing of heterogeneous data obtained using sensors of various physical nature. In the study of the forest fund, both methods of direct dendrological measurements and methods of remote observation using unmanned aerial vehicles are widely used. Information obtained using these methods must be analyzed in conjunction with hydrometeorological monitoring data.Aim. Investigation of the possibility of automating the monitoring of the well-being of the forest fund based on the integration of ground survey data, remote multispectral measurements and hydrometeorological observations using the mathematical apparatus of nonparametric Bayesian networks.Materials and methods. To assess the long-term joint dynamics of natural and climatic indicators and the radial growth of trees, a modified method of multiscale cross-correlation analysis was used with the removal of the background trend described by the moving average model. Relationships between various indicators were estimated based on the unconditional and conditional nonparametric Spearman correlation coefficients, which were used to reconstruct and parameterize the nonparametric Bayesian network.Results. A multiscale nonparametric Bayesian network was constructed to characterize both unconditional and conditional statistical relationships between parameters obtained from remote sensing, hydroclimatic and dendrological measurements. The proposed model showed a good quality of the plant fund state forecasting. The correlation coefficients between the observed and predicted indicators exceed 0.6, with the correlation coefficient comprising 0.77 when predicting the growth trend of annual tree rings.Conclusion. The proposed nonparametric Bayesian network model reflects the relationship between various factors that affect the forest ecosystem. The Bayesian network can be used to assess risks and improve environmental management planning.

A Non-parametric Bayesian Network for multivariate probabilistic modelling of Weigh-in-Motion System Data

Weigh-in-motion (WIM) systems help to collect data such as vehicular loads, individual axle loads, vehicle type, and number of axles. This is relevant in engineering because traffic load performs an essential function in the design of new bridges and in the reliability assessment of existing ones, in traffic analysis and other areas of engineering. Therefore, when WIM data is not available, computing synthetic WIM observations that adequately approximate the statistical dependence between variables is important. In this paper, WIM measurements from the Netherlands and Brazil were analysed, and a set of non-parametric Bayesian Networks (NPBNs) is presented. This paper significantly improves on previous results by allowing observations of inter-axial distance to be generated, by allowing several sources of data to be used in the modelling and by making software available to researchers and practitioners interested for generating synthetic observations based on the distribution of vehicle type. In particular, statistical models to describe the weight and length of different vehicle types are derived. Three NPBNs were quantified using data from: (i) six WIM locations of the motorway network of the Netherlands, (ii) one WIM location in one city route of Rotterdam, The Netherlands, and (iii) one WIM location of one highway in Araranguá city located in the south of Brazil. Additionally, a Graphical User Interface (GUI) for the six Dutch WIM motorways locations was developed. To illustrate a possible use of the model when WIM data is not available. The GUI was used to compute synthetic WIM observations using data collected through traffic counters gathered in Toluca city in central Mexico, as input. This paper shows that the methodology here presented is widely applicable and depends only on the assessment of vehicle type configuration.

An evolutionary event detection model using the Matrix Decomposition Oriented Dirichlet Process

With the huge expansion of user generated content on social networks, event detection has emerged as a major challenge and source of knowledge discovery. This knowledge is employed in different applications such as recommender systems, crisis management systems, and decision support systems. Dynamicity, overlapping, and evolutionary behavior are the most important issues in event detection. This paper proposes a novel evolutionary model for event detection to capture the dynamism and evolving behavior of events. The proposed method uses a matrix decomposition technique and a Dirichlet Process to detect events and handle their dynamicity. This model consists of two components, namely preliminary event detection and event evolvement tracking. The former component extracts preliminary events from the available data using the matrix decomposition method. Then, subsequent data is employed into a Non-Parametric Bayesian Network, namely Dirichlet Process Mixture Model to evolve the preliminary events. During the evolvement process, data may migrate between extracted events or new events may be discovered. The experimental results and comparisons with several recently developed approaches show the superiority of the proposed approach, and its ability to capture the evolutionary behavior of events over time.

Learning Continuous High-Dimensional Models using Mutual Information and Copula Bayesian Networks

We propose a new framework to learn non-parametric graphical models from continuous observational data. Our method is based on concepts from information theory in order to discover independences and causality between variables: the conditional and multivariate mutual information (such as \cite{verny2017learning} for discrete models). To estimate these quantities, we propose non-parametric estimators relying on the Bernstein copula and that are constructed by exploiting the relation between the mutual information and the copula entropy \cite{ma2011mutual, belalia2017testing}. To our knowledge, this relation is only documented for the bivariate case and, for the need of our algorithms, is here extended to the conditional and multivariate mutual information. This framework leads to a new algorithm to learn continuous non-parametric Bayesian network. Moreover, we use this estimator to speed up the BIC algorithm proposed in \cite{elidan2010copula} by taking advantage of the decomposition of the likelihood function in a sum of mutual information \cite{koller2009probabilistic}. Finally, our method is compared in terms of performances and complexity with other state of the art techniques to learn Copula Bayesian Networks and shows superior results. In particular, it needs less data to recover the true structure and generalizes better on data that are not sampled from Gaussian distributions.

Probabilistic Characterization of the Vegetated Hydrodynamic System Using Non-Parametric Bayesian Networks

The increasing risk of flooding requires obtaining generalized knowledge for the implementation of distinct and innovative intervention strategies, such as nature-based solutions. Inclusion of ecosystems in flood risk management has proven to be an adaptive strategy that achieves multiple benefits. However, obtaining generalizable quantitative information to increase the reliability of such interventions through experiments or numerical models can be expensive, laborious, or computationally demanding. This paper presents a probabilistic model that represents interconnected elements of vegetated hydrodynamic systems using a nonparametric Bayesian network (NPBN) for seagrasses, salt marshes, and mangroves. NPBNs allow for a system-level probabilistic description of vegetated hydrodynamic systems, generate physically realistic varied boundary conditions for physical or numerical modeling, provide missing information in data-scarce environments, and reduce the amount of numerical simulations required to obtain generalized results—all of which are critically useful to pave the way for successful implementation of nature-based solutions.

Improving early stage system design under the uncertainty in reliability-wise structure

This paper is concerned with system reliability improvement at as early as the system's embodiment design stage. Design decisions made at this stage, including component reliability specifications, will have long-term impacts on the entire lifecycle of the system, but they are also difficult to make because the knowledge of system and/or subsystem performance is most likely incomplete at this moment. Thus the system reliability function cannot be fully defined analytically. Because of this inherent uncertainty at early design stage, the traditional reliability evaluation methods, which assume deterministic reliability-wise structures and component reliability specifications, may not be appropriate in real-world applications. In this paper, a Nonparametric Bayesian Network (NPBN) approach is proposed to tackle such challenges at early design stage. The proposed method can represent the uncertainty in the structure of system reliability by combining any available information of component performance in some similar or past-generation systems. This approach can partially quantify the reliability function of the system under design and propose design change recommendations in order to achieve the targeted system reliability level.

A Digital Twin approach based on nonparametric Bayesian network for complex system health monitoring

This paper proposes a Digital Twin approach for health monitoring. In this approach, a Digital Twin model based on nonparametric Bayesian network is constructed to denote the dynamic degradation process of health state and the propagation of epistemic uncertainty. Then, a real-time model updating strategy based on improved Gaussian particle filter (GPF) and Dirichlet process mixture model (DPMM) is presented to enhance the model adaptability. On one hand, for those parameters in the nonparametric Bayesian network with prior models, the improved GPF is used to update them in real time. On the other hand, for parameters lacking a prior model, DPMM is proposed to learn hidden variables, which adaptively update the model structure and greatly reduce uncertainty. Experiments on the electro-optical system are conducted to validate the feasibility of the Digital Twin approach and verify the effectiveness of the nonparametric Bayesian network. The results of comparative experiments prove that the Digital Twin approach based on nonparametric Bayesian Network has a good model self-learning ability, which improves the accuracy of health monitoring.

Complementary Modeling of Gravel Road Traffic-Generated Dust Levels Using Bayesian Regularization Feedforward Neural Networks and Binary Probit Regression

Gravel roads require extensive maintenance and rehabilitation. That is because of the dynamic behavior of gravel road conditions. This study is aimed at investigating road condition factors affecting the traffic-generated dust on gravel roads. The study concerns Laramie County, Wyoming. The parametric binary probit regression structure and the non-parametric Bayesian regularization artificial neural network (BRANN) methods were implemented to model traffic-generated dust levels as a function of the factors that contribute to dust generation. The BRANNs method simplifies the model building process by precluding irrelevant and redundant weights of artificial neural networks (ANNs). In this study, the BRANN method is utilized with one single hidden layer using the MATLAB® function for neural networks. In the hidden layer, multiple neuron counts ranging from three to thirty were attempted. The parametric and non-parametric techniques mentioned were adopted to provide comprehensive insights into important factors that contribute to dust generation. Therefore, both techniques complement each other. A total of 206 gravel road segments were used for model building for both analyses. As per the results of the BRANN model, it was found that twenty neurons produced the most accurate results. Furthermore, it was found that the BRANN model had more variables than the binary probit regression model, whereas the probit model provided general insights into the factors affecting the dust on gravel roads such as average travel speed and soil type. Also, it would be an easy-to-use method to assist local agencies and DOT practitioners in addressing the dust problems on gravel roads.

A Nonparametric Bayesian Network Model for Predicting Corrosion Depth on Buried Pipelines

The present study develops a nonparametric Bayesian network (NPBN) model to predict the corrosion depth on buried pipelines using the pipeline age and local soil properties. The dependence structure and parameters of the NPBN model are extracted from Velázquez’s dataset, which consists of 250 samples of corrosion depths, pipeline age, and such local soil properties as the water content, redox potential, and pH value. The NPBN models the joint distribution of the corrosion depth, pipeline age, and local soil parameters by a Gaussian copula. The five-fold cross-validation is used to examine the predictive capability of the developed NPBN model. The results indicate that the predicted mean values of corrosion depths in general agree well with the corresponding field measurements, and more than 95% of the field-measured depths are within the 5 to 95 percentile range of the predicted distribution for the corrosion depth. The NPBN and the associated model mining method provide an effective data-driven approach to develop predictive models of corrosion depths using soil parameters as predictors. The developed NPBN will benefit the corrosion management of pipelines for which direct inspections are infeasible.

Monitoring Cliff Erosion with LiDAR Surveys and Bayesian Network-based Data Analysis

Cliff coasts are dynamic environments that can retreat very quickly. However, the short-term changes and factors contributing to cliff coast erosion have not received as much attention as dune coasts. In this study, three soft-cliff systems in the southern Baltic Sea were monitored with the use of terrestrial laser scanner technology over a period of almost two years to generate a time series of thirteen topographic surveys. Digital elevation models constructed for those surveys allowed the extraction of several geomorphological indicators describing coastal dynamics. Combined with observational and modeled datasets on hydrological and meteorological conditions, descriptive and statistical analyses were performed to evaluate cliff coast erosion. A new statistical model of short-term cliff erosion was developed by using a non-parametric Bayesian network approach. The results revealed the complexity and diversity of the physical processes influencing both beach and cliff erosion. Wind, waves, sea levels, and precipitation were shown to have different impacts on each part of the coastal profile. At each level, different indicators were useful for describing the conditional dependency between storm conditions and erosion. These results are an important step toward a predictive model of cliff erosion.

Redefining the climate niche of plant species: A novel approach for realistic predictions of species distribution under climate change

Climate change is increasingly affecting plant species distributions, in ways that need to be predicted. Here, in a novel prediction approach, we developed the relevant climate niche (RCN) of plants, based on thorough selection of climate variables and implementation of a non-parametric Bayesian network for climate simulations. The RCN was conditionalized to project the fate of Silene acaulis in North America under moderate (Representative Concentration Pathway 4.5; RCP4.5) and extreme (RCP8.5) short-term (2011–2040) climate scenarios. We identified a three-variable climate hypervolume for S. acaulis. Within 20 years >50% of current locations of the species will be outside the defined climate hypervolume. It could compensate for climate change in 2011–2040 through a poleward shift of 0.97 °C or an upshift of 138 m in the RCP4.5 scenario, and 1.29 °C or 184 m in the RCP8.5 scenario. These results demonstrate the benefits of redefining the climate niche of plant species in the form of a user-defined, data-validated, hierarchical network comprising only variables that are consistent with species distribution. Advantages include realism and interpretability in niche modeling, and new opportunities for predicting future species distributions under climate change.

Reliability analysis of reinforced concrete vehicle bridges columns using non-parametric Bayesian networks

In the bridge industry, current traffic trends have increased the likelihood of having the simultaneous presence of both extreme live loads and earthquake events. To date, their concurrent interaction has scarcely been systematically studied. Prevailing studies have investigated the isolated existence of either live loads or seismic actions.In an effort to fill this gap in the literature, a non-parametric Bayesian Network (BN) has been proposed. It is aimed at evaluating the conditional probability of failure for a reinforced concrete bridge column, subject simultaneously to the actions mentioned above. Based on actual data from a structure located in the State of Mexico, a Monte Carlo Simulation model was developed. This led to the construction of a BN with 17 variables.The set of variables included in the model can be categorized into three groups: acting loads, materials resistances and structure force-displacement behavior. Practitioners are then provided with a tool for unspecialized labor force to gather information in situ (e.g. Weight-In-Motion data and Schmidt hammer measurements), which can be included in the network, leading to an updated probability of failure. Moreover, this framework also serves as a quantitative tool for bridge column reliability assessments.Results from the theoretical model confirmed that the bridge column probability of failure was within the expected range reported in the literature. This reflects not only the appropriateness of its design but also the suitability of the proposed BN for reliability analysis.

A nonparametric Bayesian network approach to assessing system reliability at early design stages

It is important to predict a system’s reliability at its early design stages because modifying design to improve reliability and maintainability at a later time in the system’s lifecycle will be costly and, oftentimes, impossible. However, this early prediction is challenging because of the lack of reliability data and the incomplete knowledge of a complex system’s reliability structure. To tackle this problem, this paper presents a nonparametric Bayesian network approach. Employing nonparametric Bayesian network, the limitation of discrete Bayesian network can be overcome, and it can be used as a useful tool for decision support. The proposed methodology is applied to a case study to demonstrate its prognostic and diagnostic capabilities.

Applying a Bayesian network based on Gaussian copulas to model the hydraulic boundary conditions for hurricane flood risk analysis in a coastal watershed

In recent years significant emphasis has been placed on quantifying coastal flood hazards in the U.S. using high resolution 2-D hydrodynamic and nearshore wave models. However, these studies are computationally expensive and often neglect to consider the flooding that arises from the combined hazards of precipitation and storm surge in coastal watersheds. This paper describes a method to stochastically simulate a large number of combinations of peak storm surge and cumulative precipitation to determine the hydraulic boundary conditions for a low-lying coastal watershed draining into a semi-enclosed tidal bay. The method is computationally efficient and takes into consideration five tropical cyclone characteristics at landfall: windspeed, angle of approach, landfall location, radius of maximum winds, and forward speed. A precipitation gage network and tidal gage data were used, along with observations from over 300 tropical cyclones in the Gulf of Mexico. A Non-parametric Bayesian Network was built to generate 100,000 synthetic storm events and used as input to an empirical wind set-up model to simulate storm surge within a tidal bay and at the downstream boundary of the watershed. Based on the results, probable combinations of cumulative precipitation and peak storm surge for the watershed during hurricane conditions are determined. These boundary conditions can be easily incorporated into a coastal riverine model to determine flood risk in the watershed.

Non-parametric Bayesian networks for parameter estimation in reservoir simulation: a graphical take on the ensemble Kalman filter (part I)

Reservoir simulation models are used both in the development of new fields and in developed fields where production forecasts are needed for investment decisions. When simulating a reservoir, one must account for the physical and chemical processes taking place in the subsurface. Rock and fluid properties are crucial when describing the flow in porous media. In this paper, the authors are concerned with estimating the permeability field of a reservoir. The problem of estimating model parameters such as permeability is often referred to as a history-matching problem in reservoir engineering. Currently, one of the most widely used methodologies which address the history-matching problem is the ensemble Kalman filter (EnKF). EnKF is a Monte Carlo implementation of the Bayesian update problem. Nevertheless, the EnKF methodology has certain limitations that encourage the search for an alternative method.For this reason, a new approach based on graphical models is proposed and studied. In particular, the graphical model chosen for this purpose is a dynamic non-parametric Bayesian network (NPBN). This is the first attempt to approach a history-matching problem in reservoir simulation using a NPBN-based method. A two-phase, two-dimensional flow model was implemented for a synthetic reservoir simulation exercise, and initial results are shown. The methods’ performances are evaluated and compared. This paper features a completely novel approach to history matching and constitutes only the first part (part I) of a more detailed investigation. For these reasons (novelty and incompleteness), many questions are left open and a number of recommendations are formulated, to be investigated in part II of the same paper.