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|Created:||Dec 21, 2017 at 5:16 p.m.|
|Last updated:|| Mar 01, 2018 at 10:11 p.m.
|Citation:||See how to cite this resource|
|Content types:||Single File Content|
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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: https://doi.org/10.1016/j.jhydrol.2018.01.044.
Original run configurations:
R version = 3.3.3
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
'randomForest' (version 4.6-12)
'caret' (version 6.0-73)
|The content of this resource references||https://doi.org/10.1016/j.jhydrol.2018.01.044|
|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|
This resource was created using funding from the following sources:
|Agency Name||Award Title||Award Number|
|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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