Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{ABBA:2020:JH,
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author = "S. I. Abba and Sinan Jasim Hadi and
Saad Sh. Sammen and Sinan Q. Salih and R. A. Abdulkadir and
Quoc Bao Pham and Zaher Mundher Yaseen",
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title = "Evolutionary computational intelligence algorithm
coupled with self-tuning predictive model for water
quality index determination",
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journal = "Journal of Hydrology",
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volume = "587",
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pages = "124974",
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year = "2020",
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ISSN = "0022-1694",
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DOI = "doi:10.1016/j.jhydrol.2020.124974",
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URL = "http://www.sciencedirect.com/science/article/pii/S0022169420304340",
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keywords = "genetic algorithms, genetic programming, Water quality
index, Watershed management, Extreme Gradient Boosting,
Extreme Learning Machine, Kinta River",
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abstract = "Anthropogenic activities affect the water bodies and
result in a drastic reduction of river water quality
(WQ). The development of a reliable intelligent model
for evaluating the suitability of water remains a
challenging task facing hydro-environmental engineers.
The current study is investigated the applicability of
Extreme Gradient Boosting (XGB) and Genetic Programming
(GP) in obtaining feature importance, and then
abstracted input variables were imposed into the
predictive model (the Extreme Learning Machine (ELM))
for the prediction of water quality index (WQI). The
stand-alone modeling schema is compared with the
proposed hybrid models where the optimum variables are
supplied into the GP, XGB, linear regression (LR),
stepwise linear regression (SWLR) and ELM models. The
WQ data is obtained from the Department of Environment
(DoE) (Malaysia), and results are evaluated in terms of
determination coefficient (R2) and root mean square
error (RMSE). The results demonstrated that the hybrid
GPELM and XGBELM models outperformed the standalone GP,
XGB, and ELM models for the prediction of WQI at Kinta
River basin. A comparison of the hybrid models showed
that the predictive skill of GPELM (RMSE = 3.441
training and RMSE = 3.484 testing) over XGBELM
improving the accuracy by decreasing the values of RMSE
by 5percent and 9percent for training and testing,
respectively with regards to XGBELM (RMSE = 3.606
training and RMSE = 3.816 testing). Although
regressions are often proposed as reference models (LR
and SWLR), when combined with computational
intelligence, they still provide satisfactory results
in this study. The proposed hybrid GPELM and XGBELM
models have improved the prediction accuracy with
minimum number of input variables and can therefore
serve as reliable predictive tools for WQI at Kinta
River basin",
- }
Genetic Programming entries for
S I Abba
Sinan Jasim Hadi
Saad Sh Sammen
Sinan Q Salih
Rabiu A Abdulkadir
Quoc Bao Pham
Zaher Mundher Yaseen
Citations