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

Erfan Ghasemi Tousi (Creator)
Jennifer Guohong Duan (Creator)
Patricia M. Gundy (Creator)
Kelly R Bright (Creator)
Charles P Gerba (Creator)

Agriculture and Life Sciences, College of
Civil and Architectural Engineering and Mechanics
Hydrology and Atmospheric Science
Biosystems Engineering, Research
Environmental Science, Research
Community, Environment and Policy
Environmental Science, Instruction
Nutritional Sciences and Wellness, Research

Dataset

Description

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 ...

Date made available	2022
Publisher	University of Arizona Research Data Repository

DOI
10.25422/azu.data.21096184.v1

Access Dataset

MIT License

1 Article

Evaluation of E. coli in sediment for assessing irrigation water quality using machine learning
Tousi, E. G., Duan, J. G., Gundy, P. M., Bright, K. R. & Gerba, C. P., Dec 10 2021, In: Science of the Total Environment. 799, 149286.
Research output: Contribution to journal › Article › peer-review
25 Scopus citations

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

Description

DOI

Access Dataset

Research output

Evaluation of E. coli in sediment for assessing irrigation water quality using machine learning

Cite this