Input data for real-time street flood prediction model using machine learning, Norfolk, VA
| Authors: | |
|---|---|
| Owners: | Faria Zahura |
| Type: | Resource |
| Storage: | The size of this resource is 424.3 MB |
| Created: | Dec 13, 2019 at 5:39 p.m. |
| Last updated: | Nov 19, 2020 at 12:57 a.m. |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
|---|---|
| Views: | 1828 |
| Downloads: | 539 |
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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: | 01/01/2016 |
|---|---|
| End Date: | 12/31/2018 |
Leaflet Map data © OpenStreetMap contributors
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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/
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