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A novel machine learning application: Water quality resilience prediction Model

Overview of attention for article published in Science of the Total Environment, January 2021
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Mentioned by

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2 X users

Citations

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43 Dimensions

Readers on

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101 Mendeley
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Title
A novel machine learning application: Water quality resilience prediction Model
Published in
Science of the Total Environment, January 2021
DOI 10.1016/j.scitotenv.2020.144459
Pubmed ID
Authors

Maryam Imani, Md Mahmudul Hasan, Luiz Fernando Bittencourt, Kent McClymont, Zoran Kapelan

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 101 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 12%
Student > Master 7 7%
Student > Doctoral Student 7 7%
Unspecified 6 6%
Lecturer 6 6%
Other 17 17%
Unknown 46 46%
Readers by discipline Count As %
Engineering 22 22%
Computer Science 6 6%
Unspecified 6 6%
Earth and Planetary Sciences 4 4%
Environmental Science 4 4%
Other 10 10%
Unknown 49 49%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 January 2021.
All research outputs
#20,669,432
of 25,387,668 outputs
Outputs from Science of the Total Environment
#23,101
of 29,642 outputs
Outputs of similar age
#396,326
of 520,178 outputs
Outputs of similar age from Science of the Total Environment
#690
of 927 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 29,642 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 520,178 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 927 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.