Data and Scripts for Manuscript "Enhancing environmental monitoring through machine learning"
Abstract/Contents
- Abstract
- R code and processed input data files for manuscript results and graphics
Description
| Type of resource | software, multimedia |
|---|---|
| Date created | April 2017 - August 2018 |
Creators/Contributors
| Author | Hino, Miyuki | |
|---|---|---|
| Author | Benami, Elinor | |
| Author | Brooks, Nina |
Subjects
| Subject | manipulability |
|---|---|
| Subject | machine learning |
| Subject | prediction policy |
| Subject | water pollution |
| Subject | public resource allocation |
| Subject | E-IPER |
| Subject | Emmett Interdisciplinary Program in Environment and Resources |
| Genre | Dataset |
Bibliographic information
| Related Publication | Hino, M., Benami, E., and Brooks, N. (2018). Machine learning for environmental monitoring. Nature Sustainability. https://doi.org/10.1038/s41893-018-0142-9 |
|---|---|
| Related item | |
| Location | https://purl.stanford.edu/hr919hp5420 |
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution Share Alike 3.0 Unported license (CC BY-SA).
Preferred citation
- Preferred Citation
- Hino, M., Benami, E., and Brooks, N. (2018). Data & Scripts for Manuscript "Machine learning for environmental monitoring" . Stanford Digital Repository. Available at: https://purl.stanford.edu/hr919hp5420
Collection
Stanford Research Data
View other items in this collection in SearchWorksContact information
- Contact
- elinor@stanford.edu
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