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Peter A. Khaiter - One of the best experts on this subject based on the ideXlab platform.
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Technical assessment and evaluation of Environmental Models and software
Environmental Modelling and Software, 2011Co-Authors: Georgii A. Alexandrov, Daniel P. Ames, G. Bellocchi, Michael Bruen, Neil M.j. Crout, Marina G. Erechtchoukova, Anke Hildebrandt, F. Hoffman, Conrad Jackisch, Peter A. KhaiterAbstract:This letter details the collective views of a number of independent researchers on the technical assessment and evaluation of Environmental Models and software. The purpose is to stimulate debate and initiate action that leads to an improved quality of model development and evaluation, so increasing the capacity for Models to have positive outcomes from their use. As such, we emphasize the relationship between the model evaluation process and credibility with stakeholders (including funding agencies) with a view to ensure continued support for modelling efforts.Many journals, including EM&S, publish the results of Environmental modelling studies and must judge the work and the submitted papers based solely on the material that the authors have chosen to present and on how they present it. There is considerable variation in how this is done with the consequent risk of considerable variation in the quality and usefulness of the resulting publication. Part of the problem is that the review process is reactive, responding to the submitted manuscript. In this letter, we attempt to be proactive and give guidelines for researchers, authors and reviewers as to what constitutes best practice in presenting Environmental modelling results. This is a unique contribution to the organisation and practice of model-based research and the communication of its results that will benefit the entire Environmental modelling community. For a start, our view is that the community of Environmental modellers should have a common vision of minimum standards that an Environmental model must meet. A common vision of what a good model should be is expressed in various guidelines on Good Modelling Practice. The guidelines prompt modellers to codify their practice and to be more rigorous in their model testing. Our statement within this letter deals with another aspect of the issue - it prompts professional journals to codify the peer-review process. Introducing a more formalized approach to peer-review may discourage reviewers from accepting invitations to review given the additional time and labour requirements. The burden of proving model credibility is thus shifted to the authors. Here we discuss how to reduce this burden by selecting realistic evaluation criteria and conclude by advocating the use of standardized evaluation tools as this is a key issue that needs to be tackled.
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Technical assessment and evaluation of Environmental Models and software: Letter to the Editor
Environmental Modelling and Software, 2011Co-Authors: Georgii A. Alexandrov, G. Bellocchi, Michael Bruen, Neil M.j. Crout, Marina G. Erechtchoukova, Anke Hildebrandt, F. Hoffman, Conrad Jackisch, D. Ames, Peter A. KhaiterAbstract:This letter details the collective views of a number of independent researchers on the technical assessment and evaluation of Environmental Models and software. The purpose is to stimulate debate and initiate action that leads to an improved quality of model development and evaluation, so increasing the capacity for Models to have positive outcomes from their use. As such, we emphasize the relationship between the model evaluation process and credibility with stakeholders (including funding agencies) with a view to ensure continued support for modelling efforts. Many journals, including EM&S, publish the results of Environmental modelling studies and must judge the work and the submitted papers based solely on the material that the authors have chosen to present and on how they present it. There is considerable variation in how this is done with the consequent risk of considerable variation in the quality and usefulness of the resulting publication. Part of the problem is that the review process is reactive, responding to the submitted manuscript. In this letter, we attempt to be proactive and give guidelines for researchers, authors and reviewers as to what constitutes best practice in presenting Environmental modelling results. This is a unique contribution to the organisation and practice of model-based research and the communication of its results that will benefit the entire Environmental modelling community. For a start, our view is that the community of Environmental modellers should have a common vision of minimum standards that an Environmental model must meet. A common vision of what a good model should be is expressed in various guidelines on Good Modelling Practice. The guidelines prompt modellers to codify their practice and to be more rigorous in their model testing. Our statement within this letter deals with another aspect of the issue it prompts professional journals to codify the peer-review process. Introducing a more formalized approach to peer-review may discourage reviewers from accepting invitations to review given the additional time and labour requirements. The burden of proving model credibility is thus shifted to the authors. Here we discuss how to reduce this burden by selecting realistic evaluation criteria and conclude by advocating the use of standardized evaluation tools as this is a key issue that needs to be tackled. (C) 2010 Elsevier Ltd. All rights reserved.
Frederic Mauny - One of the best experts on this subject based on the ideXlab platform.
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assessing residential exposure to urban noise using Environmental Models does the size of the local living neighborhood matter
Journal of Exposure Science and Environmental Epidemiology, 2015Co-Authors: Quentin Tenailleau, Nadine Bernard, Sophie Pujol, Helene Houot, Daniel Joly, Frederic MaunyAbstract:: Environmental epidemiological studies rely on the quantification of the exposure level in a surface defined as the subject's exposure area. For residential exposure, this area is often the subject's neighborhood. However, the variability of the size and nature of the neighborhoods makes comparison of the findings across studies difficult. This article examines the impact of the neighborhood's definition on Environmental noise exposure levels obtained from four commonly used sampling techniques: address point, facade, buffers, and official zoning. A high-definition noise model, built on a middle-sized French city, has been used to estimate LAeq,24 h exposure in the vicinity of 10,825 residential buildings. Twelve noise exposure indicators have been used to assess inhabitants' exposure. Influence of urban Environmental factors was analyzed using multilevel modeling. When the sampled area increases, the average exposure increases (+3.9 dB), whereas the SD decreases (-1.6 dB) (P<0.01). Most of the indicators differ statistically. When comparing indicators from the 50-m and 400-m radius buffers, the assigned LAeq,24 h level varies across buildings from -9.4 to +22.3 dB. This variation is influenced by urban Environmental characteristics (P<0.01). On the basis of this study's findings, sampling technique, neighborhood size, and Environmental composition should be carefully considered in further exposure studies.Journal of Exposure Science and Environmental Epidemiology advance online publication, 28 May 2014; doi:10.1038/jes.2014.33.
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Assessing residential exposure to urban noise using Environmental Models: does the size of the local living neighborhood matter?
Journal of Exposure Science & Environmental Epidemiology, 2015Co-Authors: Quentin Tenailleau, Nadine Bernard, Sophie Pujol, Helene Houot, Daniel Joly, Frederic MaunyAbstract:Environmental epidemiological studies rely on the quantification of the exposure level in a surface defined as the subject’s exposure area. For residential exposure, this area is often the subject’s neighborhood. However, the variability of the size and nature of the neighborhoods makes comparison of the findings across studies difficult. This article examines the impact of the neighborhood’s definition on Environmental noise exposure levels obtained from four commonly used sampling techniques: address point, façade, buffers, and official zoning. A high-definition noise model, built on a middle-sized French city, has been used to estimate L_Aeq,24 _h exposure in the vicinity of 10,825 residential buildings. Twelve noise exposure indicators have been used to assess inhabitants’ exposure. Influence of urban Environmental factors was analyzed using multilevel modeling. When the sampled area increases, the average exposure increases (+3.9 dB), whereas the SD decreases (−1.6 dB) ( P
Georgii A. Alexandrov - One of the best experts on this subject based on the ideXlab platform.
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Technical assessment and evaluation of Environmental Models and software
Environmental Modelling and Software, 2011Co-Authors: Georgii A. Alexandrov, Daniel P. Ames, G. Bellocchi, Michael Bruen, Neil M.j. Crout, Marina G. Erechtchoukova, Anke Hildebrandt, F. Hoffman, Conrad Jackisch, Peter A. KhaiterAbstract:This letter details the collective views of a number of independent researchers on the technical assessment and evaluation of Environmental Models and software. The purpose is to stimulate debate and initiate action that leads to an improved quality of model development and evaluation, so increasing the capacity for Models to have positive outcomes from their use. As such, we emphasize the relationship between the model evaluation process and credibility with stakeholders (including funding agencies) with a view to ensure continued support for modelling efforts.Many journals, including EM&S, publish the results of Environmental modelling studies and must judge the work and the submitted papers based solely on the material that the authors have chosen to present and on how they present it. There is considerable variation in how this is done with the consequent risk of considerable variation in the quality and usefulness of the resulting publication. Part of the problem is that the review process is reactive, responding to the submitted manuscript. In this letter, we attempt to be proactive and give guidelines for researchers, authors and reviewers as to what constitutes best practice in presenting Environmental modelling results. This is a unique contribution to the organisation and practice of model-based research and the communication of its results that will benefit the entire Environmental modelling community. For a start, our view is that the community of Environmental modellers should have a common vision of minimum standards that an Environmental model must meet. A common vision of what a good model should be is expressed in various guidelines on Good Modelling Practice. The guidelines prompt modellers to codify their practice and to be more rigorous in their model testing. Our statement within this letter deals with another aspect of the issue - it prompts professional journals to codify the peer-review process. Introducing a more formalized approach to peer-review may discourage reviewers from accepting invitations to review given the additional time and labour requirements. The burden of proving model credibility is thus shifted to the authors. Here we discuss how to reduce this burden by selecting realistic evaluation criteria and conclude by advocating the use of standardized evaluation tools as this is a key issue that needs to be tackled.
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Technical assessment and evaluation of Environmental Models and software: Letter to the Editor
Environmental Modelling and Software, 2011Co-Authors: Georgii A. Alexandrov, G. Bellocchi, Michael Bruen, Neil M.j. Crout, Marina G. Erechtchoukova, Anke Hildebrandt, F. Hoffman, Conrad Jackisch, D. Ames, Peter A. KhaiterAbstract:This letter details the collective views of a number of independent researchers on the technical assessment and evaluation of Environmental Models and software. The purpose is to stimulate debate and initiate action that leads to an improved quality of model development and evaluation, so increasing the capacity for Models to have positive outcomes from their use. As such, we emphasize the relationship between the model evaluation process and credibility with stakeholders (including funding agencies) with a view to ensure continued support for modelling efforts. Many journals, including EM&S, publish the results of Environmental modelling studies and must judge the work and the submitted papers based solely on the material that the authors have chosen to present and on how they present it. There is considerable variation in how this is done with the consequent risk of considerable variation in the quality and usefulness of the resulting publication. Part of the problem is that the review process is reactive, responding to the submitted manuscript. In this letter, we attempt to be proactive and give guidelines for researchers, authors and reviewers as to what constitutes best practice in presenting Environmental modelling results. This is a unique contribution to the organisation and practice of model-based research and the communication of its results that will benefit the entire Environmental modelling community. For a start, our view is that the community of Environmental modellers should have a common vision of minimum standards that an Environmental model must meet. A common vision of what a good model should be is expressed in various guidelines on Good Modelling Practice. The guidelines prompt modellers to codify their practice and to be more rigorous in their model testing. Our statement within this letter deals with another aspect of the issue it prompts professional journals to codify the peer-review process. Introducing a more formalized approach to peer-review may discourage reviewers from accepting invitations to review given the additional time and labour requirements. The burden of proving model credibility is thus shifted to the authors. Here we discuss how to reduce this burden by selecting realistic evaluation criteria and conclude by advocating the use of standardized evaluation tools as this is a key issue that needs to be tackled. (C) 2010 Elsevier Ltd. All rights reserved.
B. Page - One of the best experts on this subject based on the ideXlab platform.
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A Knowledge-Based Simulation Kernel System for the Design of Environmental Modelling Tools
Environmental Informatics, 1995Co-Authors: A. Häuslein, B. PageAbstract:Modelling Environmental systems requires powerful software tools, which account for the heterogeneous Environmental domains, involving a wide ranging of modelling methodologies, and the non-technical user groups with only limited background in modelling and computing. It is a challenge for applied informatics to develop flexible software tools for the construction and utilization of heterogeneous Environmental Models [16] such as road traffic emission Models. By using an object-oriented, knowledge-based simulation system the development of new simulation methodologies such as for road traffic emission Models can be facilitated to high degree.
Keith Beven - One of the best experts on this subject based on the ideXlab platform.
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sensitivity analysis of Environmental Models
Environmental Modelling and Software, 2016Co-Authors: Francesca Pianosi, Keith Beven, Jim E Freer, Jim W Hall, Jonathan Rougier, David B Stephenson, Thorsten WagenerAbstract:Sensitivity Analysis (SA) investigates how the variation in the output of a numerical model can be attributed to variations of its input factors. SA is increasingly being used in Environmental modelling for a variety of purposes, including uncertainty assessment, model calibration and diagnostic evaluation, dominant control analysis and robust decision-making. In this paper we review the SA literature with the goal of providing: (i) a comprehensive view of SA approaches also in relation to other methodologies for model identification and application; (ii) a systematic classification of the most commonly used SA methods; (iii) practical guidelines for the application of SA. The paper aims at delivering an introduction to SA for non-specialist readers, as well as practical advice with best practice examples from the literature; and at stimulating the discussion within the community of SA developers and users regarding the setting of good practices and on defining priorities for future research. We present an overview of SA and its link to uncertainty analysis, model calibration and evaluation, robust decision-making.We provide a systematic review of existing approaches, which can support users in the choice of an SA method.We provide practical guidelines by developing a workflow for the application of SA and discuss critical choices.We give best practice examples from the literature and highlight trends and gaps for future research.
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towards integrated Environmental Models of everywhere uncertainty data and modelling as a learning process
Hydrology and Earth System Sciences, 2007Co-Authors: Keith BevenAbstract:Developing integrated Environmental Models of everywhere such as are demanded by the requirements of, for example, implementing the Water Framework Directive in Europe, is constrained by the limitations of current understanding and data availability. The possibility of such Models raises questions about system design requirements to allow modelling as a learning and data assimilation process in the representation of places, which might well be treated as active objects in such a system. Uncertainty in model predictions not only poses issues about the value of different types of data in characterising places and constraining predictive uncertainty but also about how best to present the pedigree of such uncertain predictions to users and decision-makers.
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Reply to The emergence of a new kind of relativism in Environmental modelling: a commentary by Philippe Baveye
Proceedings of The Royal Society A: Mathematical Physical and Engineering Sciences, 2004Co-Authors: Keith BevenAbstract:This article is a response to a comment by Baveye in which he invites me to expand on the use of the word relativism in the context of Environmental Models. Limitations to a classical statistical approach to model (as realist hypothesis) falsification are discussed. It is concluded that Environmental modelling must necessarily be operator dependent and relativist, even while being conducted in a context of pragmatic realism.