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Taxonomy of purposes, methods, and recommendations for vulnerability analysis.

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Title: Taxonomy of purposes, methods, and recommendations for vulnerability analysis.
Authors: Bonham, Nathan1,2,3 (AUTHOR), Kasprzyk, Joseph1,2 (AUTHOR) joseph.kasprzyk@colorado.edu, Zagona, Edith1,2 (AUTHOR)
Source: Environmental Modelling & Software. Jan2025, Vol. 183, pN.PAG-N.PAG. 1p.
Subjects: Machine learning, Watersheds, Environmental management, Decision making, Taxonomy
Abstract: Vulnerability analysis is an emerging technique that discovers concise descriptions of the conditions that lead to decision-relevant outcomes (i.e., scenarios) by applying machine learning methods to a large ensemble of simulation model runs. This review organizes vulnerability analysis methods into a taxonomy and compares them in terms of interpretability, flexibility, and accuracy. Our review contextualizes interpretability in terms of five purposes for vulnerability analysis, such as adaptation systems and choosing between policies. We make recommendations for designing a vulnerability analysis that is interpretable for a specific purpose. Furthermore, a numerical experiment demonstrates how methods can be compared based on interpretability and accuracy. Several research opportunities are identified, including new developments in machine learning that could reduce computing requirements and improve interpretability. Throughout the review, a consistent example of reservoir operation policies in the Colorado River Basin illustrates the methods. • Vulnerability analysis discovers concise descriptions of conditions that lead to decision-relevant performance outcomes. • Performance outcomes are binary, multi-class, or continuous, and methods are sorted by interpretability and flexibility. • Recommendation: use methods that maximize interpretability subject to accuracy requirements for the decision context. [ABSTRACT FROM AUTHOR]
Copyright of Environmental Modelling & Software is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 181196701
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PubType: Academic Journal
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  Data: Taxonomy of purposes, methods, and recommendations for vulnerability analysis.
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  Data: <searchLink fieldCode="AR" term="%22Bonham%2C+Nathan%22">Bonham, Nathan</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kasprzyk%2C+Joseph%22">Kasprzyk, Joseph</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> joseph.kasprzyk@colorado.edu</i><br /><searchLink fieldCode="AR" term="%22Zagona%2C+Edith%22">Zagona, Edith</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Environmental+Modelling+%26+Software%22">Environmental Modelling & Software</searchLink>. Jan2025, Vol. 183, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Watersheds%22">Watersheds</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+management%22">Environmental management</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Taxonomy%22">Taxonomy</searchLink>
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  Label: Abstract
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  Data: Vulnerability analysis is an emerging technique that discovers concise descriptions of the conditions that lead to decision-relevant outcomes (i.e., scenarios) by applying machine learning methods to a large ensemble of simulation model runs. This review organizes vulnerability analysis methods into a taxonomy and compares them in terms of interpretability, flexibility, and accuracy. Our review contextualizes interpretability in terms of five purposes for vulnerability analysis, such as adaptation systems and choosing between policies. We make recommendations for designing a vulnerability analysis that is interpretable for a specific purpose. Furthermore, a numerical experiment demonstrates how methods can be compared based on interpretability and accuracy. Several research opportunities are identified, including new developments in machine learning that could reduce computing requirements and improve interpretability. Throughout the review, a consistent example of reservoir operation policies in the Colorado River Basin illustrates the methods. • Vulnerability analysis discovers concise descriptions of conditions that lead to decision-relevant performance outcomes. • Performance outcomes are binary, multi-class, or continuous, and methods are sorted by interpretability and flexibility. • Recommendation: use methods that maximize interpretability subject to accuracy requirements for the decision context. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Environmental Modelling & Software is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=181196701
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      – Type: doi
        Value: 10.1016/j.envsoft.2024.106269
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Watersheds
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      – SubjectFull: Environmental management
        Type: general
      – SubjectFull: Decision making
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      – SubjectFull: Taxonomy
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              Text: Jan2025
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              Y: 2025
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