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Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling

Overview of attention for article published in Science of the Total Environment, December 2017
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2 tweeters

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Title
Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling
Published in
Science of the Total Environment, December 2017
DOI 10.1016/j.scitotenv.2017.07.198
Pubmed ID
Authors

Hamid Reza Pourghasemi, Saleh Yousefi, Aiding Kornejady, Artemi Cerdà

Abstract

Gully erosion is identified as an important sediment source in a range of environments and plays a conclusive role in redistribution of eroded soils on a slope. Hence, addressing spatial occurrence pattern of this phenomenon is very important. Different ensemble models and their single counterparts, mostly data mining methods, have been used for gully erosion susceptibility mapping; however, their calibration and validation procedures need to be thoroughly addressed. The current study presents a series of individual and ensemble data mining methods including artificial neural network (ANN), support vector machine (SVM), maximum entropy (ME), ANN-SVM, ANN-ME, and SVM-ME to map gully erosion susceptibility in Aghemam watershed, Iran. To this aim, a gully inventory map along with sixteen gully conditioning factors was used. A 70:30% randomly partitioned sets were used to assess goodness-of-fit and prediction power of the models. The robustness, as the stability of models' performance in response to changes in the dataset, was assessed through three training/test replicates. As a result, conducted preliminary statistical tests showed that ANN has the highest concordance and spatial differentiation with a chi-square value of 36,656 at 95% confidence level, while the ME appeared to have the lowest concordance (1772). The ME model showed an impractical result where 45% of the study area was introduced as highly susceptible to gullying, in contrast, ANN-SVM indicated a practical result with focusing only on 34% of the study area. Through all three replicates, the ANN-SVM ensemble showed the highest goodness-of-fit and predictive power with a respective values of 0.897 (area under the success rate curve) and 0.879 (area under the prediction rate curve), on average, and correspondingly the highest robustness. This attests the important role of ensemble modeling in congruently building accurate and generalized models which emphasizes the necessity to examine different models integrations. The result of this study can prepare an outline for further biophysical designs on gullies scattered in the study area.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 89 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 19%
Researcher 12 13%
Student > Doctoral Student 9 10%
Student > Master 8 9%
Lecturer 6 7%
Other 18 20%
Unknown 19 21%
Readers by discipline Count As %
Earth and Planetary Sciences 17 19%
Environmental Science 14 16%
Computer Science 10 11%
Engineering 9 10%
Agricultural and Biological Sciences 4 4%
Other 10 11%
Unknown 25 28%

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 12 August 2017.
All research outputs
#9,902,374
of 15,557,767 outputs
Outputs from Science of the Total Environment
#9,143
of 14,355 outputs
Outputs of similar age
#159,645
of 272,409 outputs
Outputs of similar age from Science of the Total Environment
#72
of 106 outputs
Altmetric has tracked 15,557,767 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,355 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one is in the 28th percentile – i.e., 28% 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 272,409 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 106 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.