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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. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 1723 |
| Downloads: | 96 |
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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.
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| The content of this resource is derived from | http://www.hydroshare.org/resource/326ce9eb2b8142519c4e09e06ecc62bd |
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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