Data for 'A day at the beach: Enabling coastal water quality prediction with high-frequency sampling and data-driven models'
Abstract/Contents
- Abstract
- The .csv files contain the modeling datasets used to train and validate the data-driven models in the project. They contain both data collected during the high-frequency (HF) sampling events and by agencies during routine monitoring (RM). They are labelled according to the study site (LP - Lover's Point; CB - Cowell Beach; HSB - Huntington State Beach). Both enterococcus and E. coli data are present as well as index environmental data (e.g. tide, waves, meteorological). Models were developed using the attached Python scripts. The subsequently trained and tested on these data, including model variables, training and validation metrics, and model pickle files can be developed using these scripts. More code can be found on the referenced GitHub account. Please contact the publishing authors for more information.
Description
| Type of resource | Dataset |
|---|---|
| Date created | September 1, 2020 |
| Date modified | September 24, 2021; December 5, 2022 |
| Publication date | May 7, 2021 |
Creators/Contributors
| Author | Searcy, Ryan T. |
|---|---|
| Author | Boehm, Alexandria B. |
Subjects
| Subject | high-frequency |
|---|---|
| Subject | data-driven models |
| Subject | fecal indicator bacteria |
| Subject | beach water quality |
| Subject | environmental data |
| Subject | civil and environmental engineering |
| Subject | Stanford University |
| Genre | Data |
| Genre | Data sets |
| Genre | Dataset |
Bibliographic information
| Related item |
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|---|---|
| Location | https://purl.stanford.edu/vh736vq8124 |
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.
Preferred citation
- Preferred citation
- Searcy, Ryan T. and Boehm, Alexandria B.. (2020). Data for 'A day at the beach: Enabling coastal water quality prediction with high-frequency sampling and data-driven models'. Stanford Digital Repository. Available at: https://purl.stanford.edu/vh736vq8124
Collection
Boehm Research Group at Stanford
View other items in this collection in SearchWorksContact information
- Contact
- rtsearcy@stanford.edu
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