Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review
Created by W.Langdon from
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- @Article{RAJAEE:2020:CILS,
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author = "Taher Rajaee and Salar Khani and Masoud Ravansalar",
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title = "Artificial intelligence-based single and hybrid models
for prediction of water quality in rivers: A review",
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journal = "Chemometrics and Intelligent Laboratory Systems",
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volume = "200",
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pages = "103978",
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year = "2020",
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ISSN = "0169-7439",
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DOI = "doi:10.1016/j.chemolab.2020.103978",
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URL = "http://www.sciencedirect.com/science/article/pii/S0169743919304939",
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keywords = "genetic algorithms, genetic programming, Artificial
intelligence, Hybrid model, Wavelet transform, River
water quality, Prediction, Review",
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abstract = "The need for accurate predictions of water quality in
rivers has encouraged researchers to develop new
methods and to improve the predictive ability of
conventional models. In recent years, artificial
intelligence (AI)-based methods have been recognized
significantly powerful for this purpose. In this study,
the performance of the various types of single and
hybrid AI models including artificial neural networks
(ANNs), genetic programming (GP), fuzzy logic (FL),
support vector machine (SVM), hybrid neuro-fuzzy (NF),
hybrid ANN-ARIMA, hybrid genetic algorithm-neural
networks (GA-NN), and wavelet-based hybrid models such
as wavelet-neural networks (WANN), wavelet-neuro fuzzy
(WNF), wavelet-support vector regression (WSVR), and
wavelet-linear genetic programming (WLGP) models were
investigated for the prediction of water quality in
rivers. In this review paper, for each of the models,
firstly, a brief introduction is provided. Then some
recently published papers are presented to review the
performance of the model for modeling water quality in
rivers. For this purpose, 51 journal papers that were
published from 2000 to 2016 and dealing with the use of
the single and hybrid AI models for river water quality
prediction were selected. The review of these papers is
undertaken in terms of the predictor selection, data
normalization, train, and test data division, modeling
approaches, prediction time steps, and modeling
performance evaluation procedures. The effect of using
integrated models to improve the prediction accuracy of
the single models was investigated as well. Out of the
51 selected papers, 31 papers (~60percent of the entire
papers) were published in the past five years. The
selected papers have been cited up to 1716 times before
20th February 2016. Among the various modeling
techniques, the ANN and WANN models (17 and 7 papers,
respectively) were the most widely used single and
hybrid models. In the reviewed papers, more attention
is given to the modeling of dissolved oxygen (DO) and
suspended sediment in rivers. In 23 papers, data with
daily time intervals were used for water quality
modeling. The present paper covers 13 different single
and hybrid AI models. It presents a comprehensive
investigation into the application of AI methods for
modeling river water quality and offers a critical
insight into the use and reliability of the various
modeling approaches for modeling diverse water quality
measurements",
- }
Genetic Programming entries for
Taher Rajaee
Salar Khani
Masoud Ravansalar
Citations