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Data-driven model script for flood severity modeling in Norfolk, VA

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Type: Resource
Storage: The size of this resource is 3.9 KB
Created: Dec 21, 2017 at 5:16 p.m.
Last updated: Mar 01, 2018 at 10:11 p.m.
DOI: 10.4211/hs.712cd2ce8f604c8f824d6836ee3fcb53
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Content types: Single File Content 
Sharing Status: Published
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This is a script written in the R programming language. The script is used to train and apply two data-driven models, Random Forest and Poisson regression. The target variable is the number of flood reports per storm event in Norfolk, VA USA. The input variables for the models are environmental conditions on an event time scale (or daily if no flood reports were made for an event). This script was used to produce results published in a paper in the Journal of Hydrology:
Original run configurations:
R version = 3.3.3
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
Packages used:
'randomForest' (version 4.6-12)
'caret' (version 6.0-73)

Subject Keywords



Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
North Latitude
East Longitude
South Latitude
West Longitude


Start Date:
End Date:


Related Resources

The content of this resource references
This resource belongs to the following collections:
Title Owners Sharing Status My Permission
Data-driven street flood severity modeling in Norfolk, Virginia USA 2010-2016 Jeff Sadler · Jonathan Goodall  Public &  Shareable Open Access


Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
Mid-Atlantic Transportation Sustainability Center

How to Cite

Sadler, J. (2018). Data-driven model script for flood severity modeling in Norfolk, VA, HydroShare,

This resource is shared under the Creative Commons Attribution CC BY.


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