Browsing by Subject "Model uncertainty"

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  • Sahlin, Ullrika; Helle, Inari; Perepolkin, Dmytro (2021)
    Failing to communicate current knowledge limitations, that is, epistemic uncertainty, in environmental risk assessment (ERA) may have severe consequences for decision making. Bayesian networks (BNs) have gained popularity in ERA, primarily because they can combine variables from different models and integrate data and expert judgment. This paper highlights potential gaps in the treatment of uncertainty when using BNs for ERA and proposes a consistent framework (and a set of methods) for treating epistemic uncertainty to help close these gaps. The proposed framework describes the treatment of epistemic uncertainty about the model structure, parameters, expert judgment, data, management scenarios, and the assessment's output. We identify issues related to the differentiation between aleatory and epistemic uncertainty and the importance of communicating both uncertainties associated with the assessment predictions (direct uncertainty) and the strength of knowledge supporting the assessment (indirect uncertainty). Probabilities, intervals, or scenarios are expressions of direct epistemic uncertainty. The type of BN determines the treatment of parameter uncertainty: epistemic, aleatory, or predictive. Epistemic BNs are useful for probabilistic reasoning about states of the world in light of evidence. Aleatory BNs are the most relevant for ERA, but they are not sufficient to treat epistemic uncertainty alone because they do not explicitly express parameter uncertainty. For uncertainty analysis, we recommend embedding an aleatory BN into a model for parameter uncertainty. Bayesian networks do not contain information about uncertainty in the model structure, which requires several models. Statistical models (e.g., hierarchical modeling outside the BNs) are required to consider uncertainties and variability associated with data. We highlight the importance of being open about things one does not know and carefully choosing a method to precisely communicate both direct and indirect uncertainty in ERA. Integr Environ Assess Manag 2020;00:1-12. (c) 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC)
  • Belis, C. A.; Karagulian, F.; Amato, F.; Almeida, M.; Artaxo, P.; Beddows, D. C. S.; Bernardoni, V.; Bove, M. C.; Carbone, S.; Cesari, D.; Contini, D.; Cuccia, E.; Diapouli, E.; Eleftheriadis, K.; Favez, O.; El Haddad, I.; Harrison, R. M.; Hellebust, S.; Hovorka, J.; Jang, E.; Jorquera, H.; Kammermeier, T.; Karl, M.; Lucarelli, F.; Mooibroek, D.; Nava, S.; Nojgaard, J. K.; Paatero, P.; Pandolfi, M.; Perrone, M. G.; Petit, J. E.; Pietrodangelo, A.; Pokorna, P.; Prati, P.; Prevot, A. S. H.; Quass, U.; Querol, X.; Saraga, D.; Sciare, J.; Sfetsos, A.; Valli, G.; Vecchi, R.; Vestenius, M.; Yubero, E.; Hopke, P. K. (2015)
    The performance and the uncertainty of receptor models (RMs) were assessed in intercomparison exercises employing real-world and synthetic input datasets. To that end, the results obtained by different practitioners using ten different RMs were compared with a reference. In order to explain the differences in the performances and uncertainties of the different approaches, the apportioned mass, the number of sources, the chemical profiles, the contribution-to-species and the time trends of the sources were all evaluated using the methodology described in Bells et al. (2015). In this study, 87% of the 344 source contribution estimates (SCEs) reported by participants in 47 different source apportionment model results met the 50% standard uncertainty quality objective established for the performance test. In addition, 68% of the SCE uncertainties reported in the results were coherent with the analytical uncertainties in the input data. The most used models, EPA-PMF v.3, PMF2 and EPA-CMB 8.2, presented quite satisfactory performances in the estimation of SCEs while unconstrained models, that do not account for the uncertainty in the input data (e.g. APCS and FA-MLRA), showed below average performance. Sources with well-defined chemical profiles and seasonal time trends, that make appreciable contributions (>10%), were those better quantified by the models while those with contributions to the PM mass close to 1% represented a challenge. The results of the assessment indicate that RMs are capable of estimating the contribution of the major pollution source categories over a given time window with a level of accuracy that is in line with the needs of air quality management. (C) 2015 The Authors. Published by Elsevier Ltd.
  • Hatwágner, Miklós F.; Vastag, Gyula; Niskanen, Vesa A.; Kóczy, László T. (Springer Verlag, 2019)
    Studies in Computational Intelligence
    Fuzzy Cognitive Map (FCMs) is an appropriate tool to describe, qualitatively analyze or simulate the behavior of complex systems. FCMs are bipolar fuzzy graphs: their building blocks are the concepts and the arcs. Concepts represent the most important components of the system, the weighted arcs define the strength and direction of cause-effect relationships among them. FCMs are created by experts in several cases. Despite the best intention the models may contain subjective information even if it was created by multiple experts. An inaccurate model may lead to misleading results, therefore it should be further analyzed before usage. Our method is able to automatically modify the connection weights and to test the effect of these changes. This way the hidden behavior of the model and the most influencing concepts can be mapped. Using the results the experts may modify the original model in order to achieve their goal. In this paper the internal operation of a department of a bank is modeled by FCM. The authors show how the modification of the connection weights affect the operation of the institute. This way it is easier to understand the working of the bank, and the most threatening dangers of the system getting into an unstable (chaotic or cyclic state) can be identified and timely preparations become possible. © Springer Nature Switzerland AG 2019.