Understanding the impact of climate change on critical infrastructure through nlp analysis of scientific literature

Tanwi Mallick; Joshua David Bergerson; Duane R. Verner; John K. Hutchison; Leslie Anne Levy; Prasanna Balaprakash

doi:10.1080/23789689.2024.2355772

Understanding the impact of climate change on critical infrastructure through nlp analysis of scientific literature

Tanwi Mallick
, Joshua David Bergerson
, Duane R. Verner
, John K. Hutchison
, Leslie Anne Levy
, Prasanna Balaprakash

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

Climate change is intensifying natural hazards, putting critical infrastructure systems at risk. The effects of climate change on critical infrastructure can be significant, and communities need to consider these risks when planning and designing infrastructure systems for the future. To that end, natural language processing (NLP) is a promising approach for analyzing large volumes of climate change and infrastructure-related scientific literature. To train a supervised model using NLP techniques, a significant subset of the corpus must be labeled into categories based on user-defined criteria, which is a time-consuming process. To expedite this process, we developed a weak supervision-based approach that leverages semantic similarity between categories and documents to generate category labels for the domain-specific corpus. In comparison with a months-long process of subject-matter expert labeling, we assign category labels to the whole corpus using weak supervision and supervised learning in 13 hours.

Original language	English
Pages (from-to)	22-39
Number of pages	18
Journal	Sustainable and Resilient Infrastructure
Volume	10
Issue number	1
DOIs	https://doi.org/10.1080/23789689.2024.2355772
State	Published - 2025

Funding

The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. http://energy.gov/downloads/doe-public-access-plan . This material is based in part upon work supported by the Laboratory Directed Research and Development (LDRD), Argonne National Laboratory. This research used resources from the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility under contract DE-AC02-06CH11357.

Keywords

BERT embedding
Climate hazards
critical infrastructures
natural language processing
weak supervision

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10.1080/23789689.2024.2355772

Cite this

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title = "Understanding the impact of climate change on critical infrastructure through nlp analysis of scientific literature",

abstract = "Climate change is intensifying natural hazards, putting critical infrastructure systems at risk. The effects of climate change on critical infrastructure can be significant, and communities need to consider these risks when planning and designing infrastructure systems for the future. To that end, natural language processing (NLP) is a promising approach for analyzing large volumes of climate change and infrastructure-related scientific literature. To train a supervised model using NLP techniques, a significant subset of the corpus must be labeled into categories based on user-defined criteria, which is a time-consuming process. To expedite this process, we developed a weak supervision-based approach that leverages semantic similarity between categories and documents to generate category labels for the domain-specific corpus. In comparison with a months-long process of subject-matter expert labeling, we assign category labels to the whole corpus using weak supervision and supervised learning in 13 hours.",

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note = "Publisher Copyright: {\textcopyright} This material is published by permission of the Argonne National Laboratory, operated by UChicago Argonne, LLC for the US Department of Energy under Contract No. DE-AC02-06CH11357. The US Government retains for itself, and others acting on its behalf, a paid-up, non-exclusive, and irrevocable worldwide licence in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.",

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doi = "10.1080/23789689.2024.2355772",

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AU - Levy, Leslie Anne

AU - Balaprakash, Prasanna

N1 - Publisher Copyright: © This material is published by permission of the Argonne National Laboratory, operated by UChicago Argonne, LLC for the US Department of Energy under Contract No. DE-AC02-06CH11357. The US Government retains for itself, and others acting on its behalf, a paid-up, non-exclusive, and irrevocable worldwide licence in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.

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AB - Climate change is intensifying natural hazards, putting critical infrastructure systems at risk. The effects of climate change on critical infrastructure can be significant, and communities need to consider these risks when planning and designing infrastructure systems for the future. To that end, natural language processing (NLP) is a promising approach for analyzing large volumes of climate change and infrastructure-related scientific literature. To train a supervised model using NLP techniques, a significant subset of the corpus must be labeled into categories based on user-defined criteria, which is a time-consuming process. To expedite this process, we developed a weak supervision-based approach that leverages semantic similarity between categories and documents to generate category labels for the domain-specific corpus. In comparison with a months-long process of subject-matter expert labeling, we assign category labels to the whole corpus using weak supervision and supervised learning in 13 hours.

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KW - weak supervision

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Understanding the impact of climate change on critical infrastructure through nlp analysis of scientific literature

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