Chapter 1 - Predicting dissolved oxygen concentration in river using new advanced machines learning: Long-short term memory (LSTM) deep learning
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- @InCollection{HEDDAM:2022:CEES,
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author = "Salim Heddam and Sungwon Kim and
Ali {Danandeh Mehr} and Mohammad Zounemat-Kermani and Anurag Malik and
Ahmed Elbeltagi and Ozgur Kisi",
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title = "Chapter 1 - Predicting dissolved oxygen concentration
in river using new advanced machines learning:
Long-short term memory ({LSTM)} deep learning",
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editor = "Hamid Reza Pourghasemi",
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booktitle = "Computers in Earth and Environmental Sciences",
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publisher = "Elsevier",
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pages = "1--20",
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year = "2022",
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isbn13 = "978-0-323-89861-4",
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DOI = "doi:10.1016/B978-0-323-89861-4.00031-2",
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URL = "https://www.sciencedirect.com/science/article/pii/B9780323898614000312",
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keywords = "genetic algorithms, genetic programming, Modeling,
Dissolved oxygen, LSTM, GP, GMDH, SVR, GRP, MLR",
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abstract = "Accurate estimation of the dissolved oxygen
concentration is critical and of significant importance
for several environmental applications. Over the years,
many types of models have been proposed to provide a
more accurate estimation of dissolved oxygen at
different time scales. Recently, the deep learning
paradigm has been increasingly used in several
environmental and engineering applications. This study
presents the application of long short-term memory
(LSTM) deep learning for dissolved oxygen (DO)
prediction in rivers. The model was trained and
calibrated using three predictors: (i) river water
temperature (Tw), (ii) air temperature, and (iii) river
discharge (Q). The variables were measured on an hourly
time scale and collected from two USGS stations. The
LSTM model was compared against genetic programming
(GP), the group method of data handling neural network
(GMDH), support vector regression (SVR), and Gaussian
process regression (GPR) models. The proposed models
were evaluated using well-known performance metrics,
namely the coefficient of correlation (R),
Nash-Sutcliffe efficiency (NSE), mean absolute error
(MAE), and root mean square error (RMSE). This study
demonstrates the utility and robustness of the proposed
models for predicting dissolved oxygen, and the GPR was
found to be slightly better than the SVR model, and
significantly better than the GMDH, LSTM, GP, and MLR
models. It was also demonstrated that the LSTM ranked
third. Numerical results showed that using Tw, Ta, and
Q as predictors combined with the periodicity (i.e.,
hour, day, and month number) leads to high accuracies
with R, NSE, RMSE, and MAE of 0.991, 0.981, 0.085, and
0.062, respectively",
- }
Genetic Programming entries for
Salim Heddam
Sungwon Kim
Ali Danandeh Mehr
Mohammad Zounemat-Kermani
Anurag Malik
Ahmed Elbeltagi
Ozgur Kisi
Citations