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Input data for real-time street flood prediction model using machine learning, Norfolk, VA
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Created: Dec 13, 2019 at 5:39 p.m.
Last updated: Nov 19, 2020 at 12:57 a.m.
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Abstract

This is tabular input data for Random Forest surrogate model built for real-time street flood prediction in Norfolk, VA, USA. The Random Forest surrogate model approximates water depth on streets generated by a 1-D pipe/2-D overland flow hydrodynamic model TUFLOW. The inputs of the model are topographic features: topographic wetness index, depth to water and elevation, and environmental features such as hourly rainfall, cumulative rainfall in previous hours, hourly tide level, etc. The output of the model is hourly water depth on streets during storm events generated by the TUFLOW model.

Subject Keywords

Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
Norfolk, VA
North Latitude
36.9714°
East Longitude
-76.1929°
South Latitude
36.8314°
West Longitude
-76.3337°

Temporal

Start Date:
End Date:

Content

How to Cite

Zahura, F. (2020). Input data for real-time street flood prediction model using machine learning, Norfolk, VA, HydroShare, http://www.hydroshare.org/resource/47a45c3185074e0e8a668babc396b4f2

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

 http://creativecommons.org/licenses/by/4.0/
CC-BY

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