Непараметрические байесовские сети как инструмент комплексирования данных мультимасштабного анализа временных рядов и дистанционного зондирования

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.Введение. Непараметрические байесовские сети представляют собой перспективный инструмент для анализа, визуализации, интерпретации и прогнозирования структурных и динамических характеристик сложных систем. Современные междисциплинарные исследования подразумевают комплексную обработку разнородных данных, получаемых с помощью датчиков различной физической природы. При исследовании лесного фонда широко применяются методы как непосредственных дендрологических измерений, так и дистанционного наблюдения с использованием беспилотных летательных аппаратов. Информацию, полученную с помощью этих методов, необходимо анализировать во взаимосвязи с данными гидрометеорологического мониторинга.Цель работы. Исследование возможности автоматизации мониторинга благополучия лесного фонда на основе комплексирования данных наземных исследований, дистанционных мультиспектральных измерений и гидрометеорологических наблюдений с использованием математического аппарата непараметрических байесовских сетей.Материалы и методы. Для оценки долговременной совместной динамики природно-климатических показателей и радиального прироста деревьев использован модифицированный метод мультимасштабного взаимного корреляционного анализа с удалением фонового тренда, описываемого моделью скользящего среднего. Взаимосвязи между различными показателями оценивались на основе безусловных и условных непараметрических коэффициентов корреляции Спирмена, которые использовались для реконструкции и параметризации непараметрической байесовской сети.Результаты. Построена мультимасштабная непараметрическая байесовская сеть, характеризующая безусловные и условные статистические взаимосвязи между параметрами, полученными в результате дистанционного зондирования, гидроклиматических и дендрологических измерений. Предложенная модель показала хорошее качество прогнозирования состояния растительного фонда. Коэффициенты корреляции между наблюдаемыми и предсказываемыми показателями превышают значения 0.6, а при предсказании тренда прироста годичных колец деревьев коэффициент корреляции составляет 0.77.Заключение. Предложенная непараметрическая байесовская сетевая модель отражает взаимосвязи между различными факторами, влияющими на лесную экосистему. Байесовская сеть может использоваться для оценки рисков и улучшения планирования экологического управления

Process safety performance indicators

For over 50 years to measure safety performance the Lost Time Incident Rate, LTIR was used. Fortunately, over the years the learning attitude towards accidents changed from a retrospective to a pro-active one. In the 90-s the safety management system was introduced. No management though, without the Deming cycle of Plan, Do, Check, Act, and checking, means the need of indicators. Existing LTIR-values were used not realizing these refl ect personal rather than process safety. In 2005 after the BP Texas City refi nery vapor cloud explosion, awareness of the difference broke through and Process Safety Leading and Lagging Metrics were formulated. In January 2012 an international conference was held in Brussels organized by EPSC and CEFIC. Results will be summarized. The paper will explain briefl y, where we are now, and what still is ahead

EEMCS final report for the causal modeling for air transport safety (CATS) project

This document reports on the work realized by the DIAM in relation to the completion of the CATS model as presented in Figure 1.6 and tries to explain some of the steps taken for its completion. The project spans over a period of time of three years. Intermediate reports have been presented throughout the project’s progress. These are presented in Appendix 1. In this report the continuous‐discrete distribution‐free BBNs are briefly discussed. The human reliability models developed for dealing with dependence in the model variables are described and the software application UniNet is presente

A multivariate framework to study spatio-temporal dependency of electricity load and wind power

With massive wind power integration, the spatial distribution of electricity load centers and wind power plants make it plausible to study the inter-spatial dependence and temporal correlation for the effective working of the power system. In this paper, a novel multivariate framework is developed to study the spatio-temporal dependency using vine copula. Hourly resolution of load and wind power data obtained from a US regional transmission operator spanning 3 years and spatially distributed in 19 load and two wind power zones are considered in this study. Data collection, in terms of dimension, tends to increase in future, and to tackle this high-dimensional data, a reproducible sampling algorithm using vine copula is developed. The sampling algorithm employs k-means clustering along with singular value decomposition technique to ease the computational burden. Selection of appropriate clustering technique and copula family is realized by the goodness of clustering and goodness of fit tests. The paper concludes with a discussion on the importance of spatio-temporal modeling of load and wind power and the advantage of the proposed multivariate sampling algorithm using vine copula

Risks out of depth? : a study on the environmental impacts of seabed mining

The oceans are facing increasing pressures from human activities. Growing industrialisation of the ocean space is giving room to both the expansion of existing and emergence of new ocean-based activities, with seabed mining one of the rapidly emerging sectors heralded as a solution to resource sufficiency. As ocean mining activities are still in exploratory stages, the development of seafloor mining is underpinned by high uncertainties on both the implementation of the activities and their consequences for the environment. Realising the full potential of the seas and oceans requires sustainable approaches to their economic development, mainly due to the issues related to the negative environmental effects, yet we lack tools and knowledge to comprehensively evaluate the impacts and further societal implications of emerging maritime sectors. To fill this gap, this thesis aims to provide a more detailed understanding of the environmental risks of seabed mining and how those risks are perceived. This thesis consists of four papers and draws on an interdisciplinary approach that includes quantitative and qualitative analyses, modelling, literature reviews and knowledge syntheses. Paper I synthesises how the environmental impacts of seabed mining have been studied in the past and draws on parallel industries, such as aggregate extraction, to increase the knowledge of the impacts on marine ecosystems. It underlines that most studies have assessed the impacts narrowly, with little appreciation of the uncertainties or cumulative effects. In this paper, I further reflect on areas that need development for comprehensive environmental risk assessments for seabed mining. Paper II contributes to the baseline information on marine mineral precipitates, estimating the distribution of ferromanganese (FeMn) concretions using spatial modelling techniques. In paper III, I develop a probabilistic modelling framework for assessing the risks of seabed mining through a series of interviews with a multidisciplinary group of experts. The risk model is then used to illustrate the impacts of FeMn concretion extraction on benthic fauna in the Baltic Sea, offering a quantitative means to highlight the many uncertainties around the impacts of mining. Paper IV examines whether people care about the impacts of human activities in remote locations. In this paper, I evaluate the dimensions of environmental care for the deep sea and relate this to the perceived risks of seafloor mining by comparing the deep sea to three other remote environments: Antarctica, the Moon, and remote terrestrial environments. The results of this work show that despite people’s low knowledge of the deep sea, people do care about mining activities harming deep-sea ecosystems, and that a stronger emotional connection to remote environments is positively connected to environmental care and perception of the severity of the risks of mining. This thesis contributes to a more comprehensive understanding of the environmental risks of seabed mining and advocates a more transparent approach to emerging industries and their risks. The combined findings of this work suggest that it is fundamental to both increase knowledge of the environment that will be affected by the risks, and to account for the underlying values and emotions towards the marine environment to fathom how those risk will be perceived. An improved appreciation of the risks of emerging maritime industries will be essential to avoid uncontrolled developments and to ensure good stewardship of the marine environment.Merialueiden kasvava käyttöpaine lisää tarvetta parantaa ympäristövaikutusten arviointikäytäntöjä. Merenpohjan kaivostoiminta on yksi nopeasti kehittyvistä aloista, jonka odotetaan vastaavan mineraalivarojen kasvavaan kysyntään muun muassa akkuteollisuuden käyttöön. Koska merenpohjan kaivoshankkeet ovat vasta kehitysvaiheessa, merenpohjan mineraalivarojen hyödyntämiseen liittyy merkittäviä epävarmuuksia johtuen sekä toiminnan toteuttamisen kehityksestä, että sen vaikutuksista meriympäristöön. Tämän väitöskirjatyön tarkoituksena on antaa yksityiskohtaisempi käsitys merenpohjan kaivostoiminnan ympäristöriskeistä. Tarkastelen erityisesti Itämeren rautamangaanisaostumia ja tämän mahdollisesti taloudellisesti, että luontoarvoiltaan merkittävän merenpohjan mineraalivarannon hyödyntämisen ympäristövaikutuksia. Työ koostuu neljästä artikkelista ja perustuu tieteidenväliseen lähestymistapaan, joka sisältää sekä määrällisiä että laadullisia analyysejä. Artikkelissa I tarkastelen, miten merenpohjan kaivostoiminnan ympäristövaikutuksia on tutkittu aiemmin ja millaisia tietoaukkoja nykyisiin arviointikäytäntöihin liittyy. Kirjallisuuskatsauksen tulokset osoittavat, että useimmat tutkimukset ovat arvioineet vaikutuksia kapeasti, eikä epävarmuustekijöitä tai kumulatiivisia vaikutuksia ole juurikaan huomioitu. Artikkelissa II arvioimme merellisten mineraalivarojen levinneisyyttä Suomen merialueilla tarkastelemalla rautamangaanisaostumien levinneisyyttä spatiaalisen mallinnuksen keinoin. Saostumia esiintyy näiden arvioiden mukaan 11–20% Suomen merialueen pohjista kaikilla Suomen merialueella lukuun ottamatta Perämerta. Artikkelissa III kehitän mallinnuskehyksen merenpohjan kaivostoiminnan ympäristöriskien arvioimiseksi. Hyödyntämällä todennäköisyysmallinnusta ja asiantuntijahaastatteluita, työssä kehitettiin uudenlainen riskiarviomenetemä, jolla voidaan tarkastella kaivostoiminnan vaikutuksia meriekosysteemin eri osiin syy-seuraus-verkostojen avulla. Tulokset osoittavat, että rajoittamattomalla ottotoiminnalla voi olla mittavia vaikutuksia meriekosysteemin toimintaan, jotka on selvitettävä ennen kuin kaupallista ottotoimintaa voidaan harkita. Artikkelissa IV tarkastelen, välittävätkö ihmiset ihmistoiminnan vaikutuksista syrjäisissä ympäristöissä. Keskityn erityisesti siihen, miten ihmiset käsittävät syvän meren kaivostoiminnan riskit vertaamalla syvää merta kolmeen muuhun kaukaiseen ympäristöön: Etelämantereeseen, Kuuhun ja maanpäällisiin syrjäisiin ympäristöihin. Tutkimuksen tulokset osoittavat ympäristön herättämien tunneyhtymien ja arvojen vaikuttavan siihen, miten paljon ihmiset välittävät ympäristöriskeistä kaukaisissa ympäristöissä, joista heillä ei ole henkilökohtaista kokemusta. Työn yhdistetyt tulokset osoittavat, että on olennaisen tärkeää sekä lisätä tietoa ympäristöstä, johon ihmistoiminnasta johtuvat riskit kohdistuvat, että ottaa huomioon ihmisten arvot ja tunteet meriympäristöä kohtaan, jotta voidaan ymmärtää, miten nämä riskit käsitetään. Kehittyvien ihmistoimintojen riskien kattavampi ymmärtäminen on välttämätöntä merialueiden hallitsemattoman teollistumisen välttämiseksi ja meriympäristön hyvän hoidon varmistamiseksi

Bayesian Network Approach to Assessing System Reliability for Improving System Design and Optimizing System Maintenance

abstract: A quantitative analysis of a system that has a complex reliability structure always involves considerable challenges. This dissertation mainly addresses uncertainty in- herent in complicated reliability structures that may cause unexpected and undesired results. The reliability structure uncertainty cannot be handled by the traditional relia- bility analysis tools such as Fault Tree and Reliability Block Diagram due to their deterministic Boolean logic. Therefore, I employ Bayesian network that provides a flexible modeling method for building a multivariate distribution. By representing a system reliability structure as a joint distribution, the uncertainty and correlations existing between system’s elements can effectively be modeled in a probabilistic man- ner. This dissertation focuses on analyzing system reliability for the entire system life cycle, particularly, production stage and early design stages. In production stage, the research investigates a system that is continuously mon- itored by on-board sensors. With modeling the complex reliability structure by Bayesian network integrated with various stochastic processes, I propose several methodologies that evaluate system reliability on real-time basis and optimize main- tenance schedules. In early design stages, the research aims to predict system reliability based on the current system design and to improve the design if necessary. The three main challenges in this research are: 1) the lack of field failure data, 2) the complex reliability structure and 3) how to effectively improve the design. To tackle the difficulties, I present several modeling approaches using Bayesian inference and nonparametric Bayesian network where the system is explicitly analyzed through the sensitivity analysis. In addition, this modeling approach is enhanced by incorporating a temporal dimension. However, the nonparametric Bayesian network approach generally accompanies with high computational efforts, especially, when a complex and large system is modeled. To alleviate this computational burden, I also suggest to building a surrogate model with quantile regression. In summary, this dissertation studies and explores the use of Bayesian network in analyzing complex systems. All proposed methodologies are demonstrated by case studies.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Identification of Causal Paths and Prediction of Runway Incursion Risk using Bayesian Belief Networks

In the U.S. and worldwide, runway incursions are widely acknowledged as a critical concern for aviation safety. However, despite widespread attempts to reduce the frequency of runway incursions, the rate at which these events occur in the U.S. has steadily risen over the past several years. Attempts to analyze runway incursion causation have been made, but these methods are often limited to investigations of discrete events and do not address the dynamic interactions that lead to breaches of runway safety. While the generally static nature of runway incursion research is understandable given that data are often sparsely available, the unmitigated rate at which runway incursions take place indicates a need for more comprehensive risk models that extend currently available research. This dissertation summarizes the existing literature, emphasizing the need for cross-domain methods of causation analysis applied to runway incursions in the U.S. and reviewing probabilistic methodologies for reasoning under uncertainty. A holistic modeling technique using Bayesian Belief Networks as a means of interpreting causation even in the presence of sparse data is outlined in three phases: causal factor identification, model development, and expert elicitation, with intended application at the systems or regulatory agency level. Further, the importance of investigating runway incursions probabilistically and incorporating information from human factors, technological, and organizational perspectives is supported. A method for structuring a Bayesian network using quantitative and qualitative event analysis in conjunction with structured expert probability estimation is outlined and results are presented for propagation of evidence through the model as well as for causal analysis. In this research, advances in the aggregation of runway incursion data are outlined, and a means of combining quantitative and qualitative information is developed. Building upon these data, a method for developing and validating a Bayesian network while maintaining operational transferability is also presented. Further, the body of knowledge is extended with respect to structured expert judgment, as operationalization is combined with elicitation of expert data to create a technique for gathering expert assessments of probability in a computationally compact manner while preserving mathematical accuracy in rank correlation and dependence structure. The model developed in this study is shown to produce accurate results within the U.S. aviation system, and to provide a dynamic, inferential platform for future evaluation of runway incursion causation. These results in part confirm what is known about runway incursion causation, but more importantly they shed more light on multifaceted causal interactions and do so in a modeling space that allows for causal inference and evaluation of changes to the system in a dynamic setting. Suggestions for future research are also discussed, most prominent of which is that this model allows for robust and flexible assessment of mitigation strategies within a holistic model of runway safety

Uncertainty analysis in product service system: Bayesian network modelling for availability contract

There is an emerging trend of manufacturing companies offering combined products and services to customers as integrated solutions. Availability contracts are an apt instance of such offerings, where product use is guaranteed to customer and is enforced by incentive-penalty schemes. Uncertainties in such an industry setting, where all stakeholders are striving to achieve their respective performance goals and at the same time collaborating intensively, is increased. Understanding through-life uncertainties and their impact on cost is critical to ensure sustainability and profitability of the industries offering such solutions. In an effort to address this challenge, the aim of this research study is to provide an approach for the analysis of uncertainties in Product Service System (PSS) delivered in business-to-business application by specifying a procedure to identify, characterise and model uncertainties with an emphasis to provide decision support and prioritisation of key uncertainties affecting the performance outcomes. The thesis presents a literature review in research areas which are at the interface of topics such as uncertainty, PSS and availability contracts. From this seven requirements that are vital to enhance the understanding and quantification of uncertainties in Product Service System are drawn. These requirements are synthesised into a conceptual uncertainty framework. The framework prescribes four elements, which include identifying a set of uncertainties, discerning the relationships between uncertainties, tools and techniques to treat uncertainties and finally, results that could ease uncertainty management and analysis efforts. The conceptual uncertainty framework was applied to an industry case study in availability contracts, where each of the four elements was realised. This application phase of the research included the identification of uncertainties in PSS, development of a multi-layer uncertainty classification, deriving the structure of Bayesian Network and finally, evaluation and validation of the Bayesian Network. The findings suggest that understanding uncertainties from a system perspective is essential to capture the network aspect of PSS. This network comprises of several stakeholders, where there is increased flux of information and material flows and this could be effectively represented using Bayesian Networks

EVALUACIÓN DE COSTOS POR FALLAS EN PRESAS DE TIERRA: UN CASO EN EL ESTADO DE MÉXICO

Miembro de un proyecto de investigación para utilizar las redes bayesianas no paramétricas en la evaluación de la seguridad de presas. Centrada en los costos directos e indirectos de la falla de una de estas estructuras.En esta tesis se propone una metodología para cuantificar los costos directos e indirectos provocados por una falla, con base en el caso de una presa de tierra en el Estado de México (Antonio Alzate). Además, se hace uso de Redes Bayesianas para cuantificar la relación entre cuatro variables: inundación, pérdidas humanas, daños económicos y daños ambientales. Finalmente, con base en la extracción sistemática de información a un grupo de expertos, se construye un modelo matemático que permite calcular las consecuencias económicas debidas al colapso de una presa, en función de la magnitud de una inundación

ANÁLISIS DE RIESGO Y CONFIABILIDAD EN PRESAS DE TIERRA: UN CASO EN EL ESTADO DE MÉXICO

Miembro del conjunto de investigaciones destinadas a aplicar la teoría de las redes bayesianas en el análisis de falla de presas de tierra, enfocándose primariamente a la estabilidad de taludes.El objetivo principal de la presente investigación es analizar el riesgo y la confiabilidad de una de las principales obras de infraestructura con las que México cuenta, las presas de tierra. Esto, con base en las metodologías actuales y mediante la construcción de un modelo matemático general, que permita representar el comportamiento de las presas de tierra en el Estado de México ante diversos eventos que podrían poner en riesgo la estabilidad de la estructura. Cabe mencionar que el estudio se limita a la ocurrencia de fenómenos de carácter natural sobre la cortina de la presa, ya que es la principal estructura que es susceptible de daño, al presentarse uno o diversos eventos