Main content

Contributors:
Affiliated institutions: University of Colorado Boulder

Date created: | Last Updated:

: DOI | ARK

Creating DOI. Please wait...

Create DOI

Category: Analysis

Description: This paper takes a new look at dam hazard potential classification via machine learning algorithms by proposing a novel geospatial model to estimate new predictors. We take a multi-objective approach to the process of machine learning hyperparameter tuning. We show that such an approach gives analysts more insights into the classification problem as well as justification for hyperparameter selection. We demonstrate this framework on dams in Massachusetts, United States.

License: MIT License

Wiki

Add important information, links, or images here to describe your project.

Files

Loading files...

Citation

Components

Spatial Features Model


Recent Activity

Loading logs...

Phase 1 Optimization


Recent Activity

Loading logs...

Phase 2 Assessment


Recent Activity

Loading logs...

Tags

Recent Activity

Loading logs...

OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
Accept
This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.
Accept
×

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.