Faria Zahura
University of Virginia
Recent Activity
ABSTRACT:
The tabular input and output data and script for the 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 during pluvial and tidal flood events generated by a 1-D pipe/2-D overland flow hydrodynamic model TUFLOW. The model inputs 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 model's output is hourly water depth on streets during flood events generated by the TUFLOW model.
ABSTRACT:
This is the tabular output data from Random Forest surrogate model used to predict hourly water depth on streets during storm events in Norfolk, VA, USA. This include hourly water depth prediction made by the Random Forest model on testing storm events.
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.
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.
ABSTRACT:
This is a HEC-HMS model for a watershed draining to Rapidian river near Ruckerville, VA which discharges through USGS station 01665500. The model was calibrated using storm event occurring at Piedmont Research Station which is 18 miles downstream to the USGS station.
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Created: Nov. 21, 2017, 2:50 p.m.
Authors: Faria Zahura
ABSTRACT:
This is a HEC-HMS model for a watershed draining to Rapidian river near Ruckerville, VA which discharges through USGS station 01665500. The model was calibrated using storm event occurring at Piedmont Research Station which is 18 miles downstream to the USGS station.

Created: Dec. 13, 2019, 5:39 p.m.
Authors: Zahura, Faria
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.

Created: Dec. 13, 2019, 7:55 p.m.
Authors: Zahura, Faria
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.

Created: Dec. 15, 2019, 11:49 p.m.
Authors: Zahura, Faria
ABSTRACT:
This is the tabular output data from Random Forest surrogate model used to predict hourly water depth on streets during storm events in Norfolk, VA, USA. This include hourly water depth prediction made by the Random Forest model on testing storm events.

Created: Oct. 13, 2021, 1:58 p.m.
Authors: Zahura, Faria
ABSTRACT:
The tabular input and output data and script for the 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 during pluvial and tidal flood events generated by a 1-D pipe/2-D overland flow hydrodynamic model TUFLOW. The model inputs 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 model's output is hourly water depth on streets during flood events generated by the TUFLOW model.