A machine learning approach to identify barriers in stream networks demonstrates high prevalence of unmapped riverine dams Article Swipe
Brian Buchanan
,
Suresh A. Sethi
,
Scott Cuppett
,
Megan E. Lung
,
George Jackman
,
Liam J. Zarri
,
Ethan S. Duvall
,
Jeremy Dietrich
,
Patrick J. Sullivan
,
Alon Dominitz
,
J. A. Archibald
,
Alexander S. Flecker
,
Brian G. Rahm
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1016/j.jenvman.2021.113952
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1016/j.jenvman.2021.113952
Restoring stream ecosystem integrity by removing unused or derelict dams has become a priority for watershed conservation globally. However, efforts to restore connectivity are constrained by the availability of accurate dam inventories which often overlook smaller unmapped riverine dams. Here we develop and test a machine learning approach to identify unmapped dams using a combination of publicly available topographic and geospatial habitat data. Specifically, we trained a random forest classification algorithm to identify unmapped dams using digitally engineered predictor variables and known dam sites for validation. We applied our algorithm to two subbasins in the Hudson River watershed, USA, and quantified connectivity impacts, as well as evaluated a range of predictor sets to examine tradeoffs between classification accuracy and model parameterization effort. The random forest classifier achieved high accuracy in predicting dam sites (true positive rate = 89%, false positive rate = 1.2%) using a subset of variables related to stream slope and presence of upstream lentic habitats. Unmapped dams were prevalent throughout the two test watersheds. In fact, existing dam inventories underestimated the true number of dams by ∼80-94%. Accounting for previously unmapped dams resulted in a 62-90% decrease in dendritic connectivity indices for migratory fishes. Unmapped dams may be pervasive and can dramatically bias stream connectivity information. However, we find that machine learning approaches can provide an accurate and scalable means of identifying unmapped dams that can guide efforts to develop accurate dam inventories, thereby informing and empowering efforts to better manage them.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jenvman.2021.113952
- OA Status
- hybrid
- Cited By
- 25
- References
- 72
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3212596683
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3212596683Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.jenvman.2021.113952Digital Object Identifier
- Title
-
A machine learning approach to identify barriers in stream networks demonstrates high prevalence of unmapped riverine damsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-11-08Full publication date if available
- Authors
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Brian Buchanan, Suresh A. Sethi, Scott Cuppett, Megan E. Lung, George Jackman, Liam J. Zarri, Ethan S. Duvall, Jeremy Dietrich, Patrick J. Sullivan, Alon Dominitz, J. A. Archibald, Alexander S. Flecker, Brian G. RahmList of authors in order
- Landing page
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https://doi.org/10.1016/j.jenvman.2021.113952Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.jenvman.2021.113952Direct OA link when available
- Concepts
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Random forest, Watershed, Habitat, Stream restoration, Geospatial analysis, Machine learning, Environmental science, Hydrology (agriculture), Geography, Environmental resource management, Artificial intelligence, Computer science, Cartography, Ecology, Engineering, Geotechnical engineering, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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25Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 5, 2023: 5, 2022: 7, 2021: 1Per-year citation counts (last 5 years)
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72Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.algorithm | 70, 89 |
| abstract_inverted_index.available | 57 |
| abstract_inverted_index.dendritic | 191 |
| abstract_inverted_index.digitally | 76 |
| abstract_inverted_index.ecosystem | 2 |
| abstract_inverted_index.evaluated | 106 |
| abstract_inverted_index.globally. | 17 |
| abstract_inverted_index.habitats. | 157 |
| abstract_inverted_index.informing | 237 |
| abstract_inverted_index.integrity | 3 |
| abstract_inverted_index.migratory | 195 |
| abstract_inverted_index.pervasive | 201 |
| abstract_inverted_index.predictor | 78, 110 |
| abstract_inverted_index.prevalent | 161 |
| abstract_inverted_index.subbasins | 92 |
| abstract_inverted_index.tradeoffs | 114 |
| abstract_inverted_index.variables | 79, 147 |
| abstract_inverted_index.watershed | 15 |
| abstract_inverted_index.Accounting | 180 |
| abstract_inverted_index.approaches | 215 |
| abstract_inverted_index.classifier | 125 |
| abstract_inverted_index.empowering | 239 |
| abstract_inverted_index.engineered | 77 |
| abstract_inverted_index.geospatial | 60 |
| abstract_inverted_index.predicting | 130 |
| abstract_inverted_index.previously | 182 |
| abstract_inverted_index.quantified | 100 |
| abstract_inverted_index.throughout | 162 |
| abstract_inverted_index.watershed, | 97 |
| abstract_inverted_index.∼80-94%. | 179 |
| abstract_inverted_index.combination | 54 |
| abstract_inverted_index.constrained | 24 |
| abstract_inverted_index.identifying | 224 |
| abstract_inverted_index.inventories | 31, 171 |
| abstract_inverted_index.topographic | 58 |
| abstract_inverted_index.validation. | 85 |
| abstract_inverted_index.watersheds. | 166 |
| abstract_inverted_index.availability | 27 |
| abstract_inverted_index.connectivity | 22, 101, 192, 207 |
| abstract_inverted_index.conservation | 16 |
| abstract_inverted_index.dramatically | 204 |
| abstract_inverted_index.information. | 208 |
| abstract_inverted_index.inventories, | 235 |
| abstract_inverted_index.Specifically, | 63 |
| abstract_inverted_index.classification | 69, 116 |
| abstract_inverted_index.underestimated | 172 |
| abstract_inverted_index.parameterization | 120 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5080810030 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I192389796 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.91212349 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |