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Jeff Sadler

University of Virginia | PhD Candidate

 Recent activity

ABSTRACT:

This is a SWMM5 model that used for demonstrating swmm_mpc, a Python package for simulating model predictive control using SWMM5 as the process model. swmm_mpc is on github: https://github.com/UVAdMIST/swmm_mpc. To run these, you will need to install the swmm_mpc python package or use the Docker image according to the instructions on the github repo readme.

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ABSTRACT:

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

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ABSTRACT:

Diagram depicting the relationship between 10 different HydroShare resources used to produce results for data-driven street flood severity modeling done for Norfolk, VA for 2010-2016. The analysis is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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ABSTRACT:

Script and accompanying ipython notebook written in Python 2.7 for aggregating sub-daily environmental data (rainfall, tide, wind, groundwater) to a daily timescale. The input data are from Norfolk, Virginia. Several different methods of aggregation are used including averages and maximums. The processed/aggregated data are combined with street flood report data to be used in data-driven, predictive modeling. The script in this resource was used in the analysis described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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ABSTRACT:

Daily observations data for rainfall, wind, tide, and water table levels. These variables are more fully defined in the raw source data. These data are used as input for data-driven prediction of street flood severity in Norfolk, VA 2010-2016. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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Resources
All 0
Collection 0
Composite Resource 0
Generic 0
Geographic Feature 0
Geographic Raster 0
HIS Referenced Time Series 0
Model Instance 0
Model Program 0
MODFLOW Model Instance Resource 0
Multidimensional (NetCDF) 0
Script Resource 0
SWAT Model Instance 0
Time Series 0
Web App 0
Composite Resource Composite Resource
Hampton Roads Environmental Time Series Data
Created: July 20, 2017, 7:18 p.m.
Authors: Jeff Sadler

ABSTRACT:

This is raw environmental time series data stored in a sqlite database with a data schema loosely based off of ODM1.1. This scheme is shown in the data model figure included in the resource. The geographical location of these data is in the Hampton Roads region in South East Virginia. The variables of the time series are rainfall, tide, wind, and water table elevations. These data were processed and used as input for data-driven modeling for street flood severity prediction. The processing and modeling are described in this Journal of Hydrology Paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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Generic Generic
Daily aggregation notebook
Created: July 20, 2017, 7:32 p.m.
Authors: Jeff Sadler

ABSTRACT:

Python 2 Jupyter notebook that aggregates sub-daily time series observations up to a daily time scale. The code was originally written to aggregate data stored in the sqlite database stored in this resource: https://www.hydroshare.org/resource/9e1b23607ac240588ba50d6b5b9a49b5/

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Composite Resource Composite Resource
Hampton Roads Flood Model Data
Created: July 21, 2017, 3:21 p.m.
Authors: Jeff Sadler

ABSTRACT:

sqlite database containing data related to street flooding in Norfolk, Virginia USA. The tables include pre- and post-processed data for machine learning models. The raw data that are preprocessed are found in this resource: https://www.hydroshare.org/resource/9e1b23607ac240588ba50d6b5b9a49b5/. The pre-processing is done via a Python 2 Jupyter notebook stored in this resource: https://www.hydroshare.org/resource/e46c995f38194c41934930a10079042b/. The preprocessed data are used in the model stored in this HS resource: https://www.hydroshare.org/resource/ae53ae6bd4374dd1a292b3555b9fa5f7/.

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Composite Resource Composite Resource
Model Flood Counts with Random Forest
Created: July 21, 2017, 3:22 p.m.
Authors: Jeff Sadler

ABSTRACT:

A Python 2 Jupyter notebook that models flood counts using the Random Forest model. The input and output data are from Norfolk, Virginia USA.

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Composite Resource Composite Resource
Flood Data DB Script
Created: July 21, 2017, 3:26 p.m.
Authors: Jeff Sadler

ABSTRACT:

Helper functions for accessing data stored in an sqlite database. There are generic functions, such as one to read a database table into a Pandas dataframe . There are also more specific functions designed to interface specifically with the data schema implemented in the database in this HydroShare resource: https://www.hydroshare.org/resource/9e1b23607ac240588ba50d6b5b9a49b5/.

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Collection Resource Collection Resource

ABSTRACT:

This resource aggregates several resources related to street flood severity modeling in Norfolk, Virginia USA. The resources include raw and pre-processed data, scripts used to perform the pre-processing, scripts used to train data-driven algorithms, and results from the models. The models used crowd-sourced street flood reports as target values and environmental data as input values. The resources in this aggregate resource are used to generate the results for this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

A diagram showing how these resources relate is shown in the "Resource workflow diagram for street flood severity modeling in Norfolk, VA 2010-2016" resource.

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Generic Generic
RF Flood modeling result figures
Created: July 24, 2017, 7:12 p.m.
Authors: Jeff Sadler

ABSTRACT:

Figures resulting from RF modeling of flood counts

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Composite Resource Composite Resource

ABSTRACT:

This is tabular output data from two data-driven models used to predict flood severity, Poisson regression and Random Forest regression. Both outputs from the training and testing phases of the modeling are included in the resource. Additionally, results indicating the relative importance of each predictor variable in the Random Forest model are provided in the "rf_impo_out.csv" file. This work is described in the following paper published in the Journal of Hydrology: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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Composite Resource Composite Resource
Input data for flood severity modeling in Norfolk, VA
Created: Dec. 21, 2017, 5:14 p.m.
Authors: Jeff Sadler

ABSTRACT:

This is tabular input data originally used in two data-driven models (Poisson regression and Random Forest) for predicting flood severity. The inputs to the model (or predictor variables) are environmental conditions such as cumulative rainfall, high and low tides, etc. The outputs (or target variable) of the model is the number of flood reports per storm event. This data was used in work that is described in the following paper published in the Journal of Hydrology: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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Composite Resource Composite Resource

ABSTRACT:

This is a script written in the R programming language. The script is used to train and apply two data-driven models, Random Forest and Poisson regression. The target variable is the number of flood reports per storm event in Norfolk, VA USA. The input variables for the models are environmental conditions on an event time scale (or daily if no flood reports were made for an event). This script was used to produce results published in a paper in the Journal of Hydrology: https://doi.org/10.1016/j.jhydrol.2018.01.044.
---
Original run configurations:
R version = 3.3.3
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
Packages used:
'randomForest' (version 4.6-12)
'caret' (version 6.0-73)

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Composite Resource Composite Resource
Raw street flood report data from Norfolk, VA 2010-2016
Created: Jan. 2, 2018, 9:20 p.m.
Authors: Jeff Sadler

ABSTRACT:

Street flooding reports made by mostly City of Norfolk staff from 2010-2016. The coordinate system used for the X and Y coordinates is "Virginia state plane, south zone, feet (NAD83)." These data were processed and used as target values for street data-driven flood prediction severity modeling. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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Composite Resource Composite Resource
Script for processing street flood reports
Created: Jan. 2, 2018, 9:22 p.m.
Authors: Jeff Sadler

ABSTRACT:

Script and accompanying notebook written in Python 2.7 for processing street flood reports made by City of Norfolk staff. The output data from this script were used as target values for street data-driven flood prediction severity modeling. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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Composite Resource Composite Resource
Processed street flood data from Norfolk, VA 2010-2016
Created: Jan. 2, 2018, 9:24 p.m.
Authors: Jeff Sadler

ABSTRACT:

Processed street flooding data from street flood reports made by City of Norfolk, VA staff 2010-2016. These data were used as target values for street data-driven flood prediction severity modeling. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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Composite Resource Composite Resource

ABSTRACT:

Script and accompanying notebook written in Python 2.7 for combining flood report data (output) and environmental data (input) into a format suitable for a data-driven model. These data used as target values for street data-driven flood prediction severity modeling for Norfolk, VA 2010-2016. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

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Composite Resource Composite Resource
Daily environmental observations Norfolk, VA 2010-2016
Created: Jan. 2, 2018, 9:37 p.m.
Authors: Jeff Sadler

ABSTRACT:

Daily observations data for rainfall, wind, tide, and water table levels. These variables are more fully defined in the raw source data. These data are used as input for data-driven prediction of street flood severity in Norfolk, VA 2010-2016. This modeling is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

Show More
Composite Resource Composite Resource

ABSTRACT:

Script and accompanying ipython notebook written in Python 2.7 for aggregating sub-daily environmental data (rainfall, tide, wind, groundwater) to a daily timescale. The input data are from Norfolk, Virginia. Several different methods of aggregation are used including averages and maximums. The processed/aggregated data are combined with street flood report data to be used in data-driven, predictive modeling. The script in this resource was used in the analysis described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

Show More
Composite Resource Composite Resource

ABSTRACT:

Diagram depicting the relationship between 10 different HydroShare resources used to produce results for data-driven street flood severity modeling done for Norfolk, VA for 2010-2016. The analysis is described in this Journal of Hydrology paper: https://doi.org/10.1016/j.jhydrol.2018.01.044.

Show More
Composite Resource Composite Resource
Plot results from data-driven street flood severity models
Created: July 13, 2018, 6:53 p.m.
Authors: Jeff Sadler

ABSTRACT:

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

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Composite Resource Composite Resource
swmm_mpc demonstration model
Created: Dec. 11, 2018, 9:41 p.m.
Authors: Jeff Sadler

ABSTRACT:

This is a SWMM5 model that used for demonstrating swmm_mpc, a Python package for simulating model predictive control using SWMM5 as the process model. swmm_mpc is on github: https://github.com/UVAdMIST/swmm_mpc. To run these, you will need to install the swmm_mpc python package or use the Docker image according to the instructions on the github repo readme.

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