Script for real-time street flood prediction model using machine learning, Norfolk, VA

Authors:
Faria Zahura
Owners: Faria Zahura
  • Contributions
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Type: Resource
Storage: The size of this resource is 5.7 KB
Created: Dec 13, 2019 at 7:55 p.m.
Last updated: Jun 17, 2020 at 12:19 a.m.
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Sharing Status: Public
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Downloads: 104
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Abstract

This is a python script used to train and test a Random Forest 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, USA
North Latitude
36.9703°
East Longitude
-76.1711°
South Latitude
36.8292°
West Longitude
-76.3345°

Temporal

Start Date: 01/01/2016
End Date: 12/31/2018
Leaflet Map data © OpenStreetMap contributors

Content

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Related Resources

The content of this resource is derived from http://www.hydroshare.org/resource/326ce9eb2b8142519c4e09e06ecc62bd

How to Cite

Zahura, F. (2020). Script for real-time street flood prediction model using machine learning, Norfolk, VA, HydroShare, http://www.hydroshare.org/resource/981253b3fbf5465fa11e0694c0015552

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

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

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