Abstract
Within the United States’ National Inventory of Dams, 15,000 dams have been classified as having a high hazard potential, meaning failure or misoperation would lead to probable loss of human life. However, state dam officials evaluate dam hazard potential on a case-by-case basis, ultimately relying on human judgement. Such a process lacks objectivity and consistency across state boundaries and can be time-consuming. Here, the authors present a parameterized geospatial and machine learning dam hazard potential classification model to overcome these limitations. The parameters of this model can be tuned for optimal performance. However, for this classification problem, the regulatory and physical implications of the types of model misclassifications are best captured through multiple objectives. Therefore, this research additionally contributes a novel multiobjective approach to machine learning parameter tuning. This research demonstrates the utility of this approach for dams in Massachusetts, United States, using a multiobjective evolutionary algorithm to explore different model parameterizations and identify analyst-relevant tradeoffs among objectives describing model performance. Such an approach allows for greater justification of model parameters as well as greater insights into the complexities of the dam hazard potential classification problem.
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Data Availability Statement
All supporting data and code are available at https://osf.io/vyzh8/.
Acknowledgments
This work utilized resources from the University of Colorado Boulder Research Computing Group, which is supported by the National Science Foundation (awards ACI-1532235 and ACI-1532236), the University of Colorado Boulder, and Colorado State University. This work acknowledges support by the US Department of Education’s Graduate Assistance in Areas of National Need (GAANN) program under Grant No. P200A180024. The authors would also like to thank the reviewers of this paper for their insights and constructive feedback. Additionally, the authors would like to thank the Kasprzyk and Baker research groups for their help and support.
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Journal of Water Resources Planning and Management
Volume 147 • Issue 10 • October 2021
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© 2021 American Society of Civil Engineers.
History
Received: Sep 11, 2020
Accepted: Mar 3, 2021
Published online: Jul 23, 2021
Published in print: Oct 1, 2021
Discussion open until: Dec 23, 2021
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