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Machine learning for environmental monitoring

Hino, M., Benami, E., Brooks, N.·2018·Nature Sustainability

DOI 10.1038/s41893-018-0142-9

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BackgroundSelf-citation
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Background
Machine Learning in Dam Water Research: An Overview of Applications and Approaches
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“Therefore, applying machines learning approach enable dam operators to improve efficiency in decision making even with limited data [23].”
Background

Authors: Hino, M., Benami, E., & Brooks, N.
Affiliations: Stanford University (US)
Corresponding author: Benami, E.
ORCID: Hino, M.
Benami, E.
Brooks, N.
Venue: Nature Sustainability
Publisher: Springer Science and Business Media LLC
Published: 2018-10-01
Volume: 1
Issue: 10
Pages: 583-588
ISSN: 2398-9629 (ISSN-L)
DOI: 10.1038/s41893-018-0142-9
OpenAlex: W2894463230
Cited by: 201
References: 17
Citations by year: 2026: 27 · 2025: 55 · 2024: 41 · 2023: 22 · 2022: 19 · 2021: 16 · 2020: 8 · 2019: 11 · 2018: 1
FWCI: 15.04
Type: journal article
Language: en
Open access: closed
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Sustainability (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Topics: Social Sciences › Public Administration › Regulation and Compliance Studies
Social Sciences › Law › Artificial Intelligence in Law
Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords: environmental law enforcement, Clean Water Act, resource allocation, gaming the system, regulatory compliance, environmental monitoring, big data, predictive analytics, Environmental Protection Agency

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