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Type: | Resource | |
Storage: | The size of this resource is 456.5 KB | |
Created: | Dec 21, 2017 at 5:14 p.m. | |
Last updated: | Mar 01, 2018 at 10:10 p.m. (Metadata update) | |
Published date: | Mar 01, 2018 at 10:10 p.m. | |
DOI: | 10.4211/hs.ff8be5aea3224c15b262bfddd5fb6033 | |
Citation: | See how to cite this resource | |
Content types: | Single File Content |
Sharing Status: | Published |
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Views: | 2342 |
Downloads: | 79 |
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Abstract
This is tabular input data originally used in two data-driven models (Poisson regression and Random Forest) for predicting flood severity. The inputs to the model (or predictor variables) are environmental conditions such as cumulative rainfall, high and low tides, etc. The outputs (or target variable) of the model is the number of flood reports per storm event. This data was used in work that is described in the following paper published in the Journal of Hydrology: https://doi.org/10.1016/j.jhydrol.2018.01.044.
Subject Keywords
Coverage
Spatial
Temporal
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Related Resources
The content of this resource was created by a related App or software program | https://www.hydroshare.org/resource/5db7884111fb4662a13f64707c0c6890/ |
This resource is referenced by | https://www.hydroshare.org/resource/712cd2ce8f604c8f824d6836ee3fcb53/ |
The content of this resource references | https://doi.org/10.1016/j.jhydrol.2018.01.044 |
The content of this resource is derived from | https://www.hydroshare.org/resource/fb2775104b554f4da0dc1045192caf6f/ |
The content of this resource is derived from | https://www.hydroshare.org/resource/6c9e9239f70a421b8c49ae642a7b9291/ |
Title | Owners | Sharing Status | My Permission |
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Data-driven street flood severity modeling in Norfolk, Virginia USA 2010-2016 | Jeff Sadler · Jonathan Goodall | Public & Shareable | Open Access |
Credits
Funding Agencies
This resource was created using funding from the following sources:
Agency Name | Award Title | Award Number |
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Mid-Atlantic Transportation Sustainability Center |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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