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Plot results from data-driven street flood severity models

Authors: Jeff Sadler
Owners: Jeff Sadler
Resource type:Composite Resource
Created:Jul 13, 2018 at 6:53 p.m.
Last updated: Jul 13, 2018 at 7:25 p.m. by Jeff Sadler


This is a Python script used to plot results from a street flood severity model. The script plots predicted flood reports against true flood reports and was originally used for making a plot for a Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044. The data files used to produce the plot for the paper are found in another HydroShare resource: https://www.hydroshare.org/resource/54df00b15c02458685fa3b622f2ecc7b/. For the script to work as is, the script has to be in the same directory as the data files and the files have to be named as follows: "poisson_[suffix]_train", "poisson_[suffix]_test", "rf_[suffix]_train", "rf_[suffix]_test". The "suffix" value should be the same as the suffix specified when using the R code that produces the data files. This code is also part of a HydroShare resource: https://www.hydroshare.org/resource/712cd2ce8f604c8f824d6836ee3fcb53/. The script is used as follows "python plot_count_model_results.py [suffix]".

Python version 2.7
Required matplotlib, pandas, and numpy

Subject Keywords

Python,Street flooding,Coastal flooding,Data-driven modeling,Plotting

How to cite

Sadler, J. (2018). Plot results from data-driven street flood severity models, HydroShare, http://www.hydroshare.org/resource/b56fea2856bc468bb0e32fc0ad8f910e

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


Sharing status:

  • Public Resource  Public
  • Sharable Resource  Shareable


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The people or organizations that created the intellectual content of the resource.

Name Organization Address Phone Author Identifiers
Jeff Sadler University of Virginia
This resource belongs to the following collections:
Title Owners Sharing Status My Permission
Data-driven street flood severity modeling in Norfolk, Virginia USA 2010-2016 Jeff Sadler · Jonathan Goodall  Public &  Shareable Open Access


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