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# Data and Code for evaluation of E. Coli in sediment for assessing irrigation water quality using machine learning

Preferred citation (DataCite format):

  Duan, Jennifer Guohong; Tousi, Erfan; Gundy, Patricia M.; Bright, Kelly; Gerba, Charles P. (2022).
  Data and Code for evaluation of E. Coli in sediment for assessing irrigation water quality using machine learning.
  University of Arizona Research Data Repository.
  Software. https://doi.org/10.25422/azu.data.21096184.


Corresponding Author:
  Jennifer Guohong Duan, Civil and Architectural Engineering, gduan@arizona.edu


License:
  MIT (code)
  CC By 4.0 (data)

DOI:
  https://doi.org/10.25422/azu.data.21096184



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## Summary

This data repository contains the field collected data of sediment quality in
irrigation canals. The Python code is to used in the paper entitled
"Evaluation of E. Coli in Sediment for assessing irrigation water quality"
published in the Science of the Total Environment Journal.

This Python program needs the input data in Excel. Data used in the paper 
is included in the Excel file. The sampling locations were removed to protect 
the privacy of land owners. 

Refer to Table 1 of the associated publication for an explanation of the 
variables in the Excel file.


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## Materials and Methods

The program is a Python code. Users need to install required library package 
to run the code. The input file format is Excel.



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## Additional Notes

Links:
  - https://www.sciencedirect.com/science/article/pii/S004896972104359X