Evaluating Machine Learning Techniques for Predicting Salinity
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{harper:2024:CEC,
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author = "Matthew Harper and Ivy Liu and Bing Xue and
Ross Vennell and Mengjie Zhang",
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title = "Evaluating Machine Learning Techniques for Predicting
Salinity",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, Training,
Accuracy, Statistical analysis, Salinity (geophysical),
System performance, Machine learning, Estuaries",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10612099",
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abstract = "Oyster farms provide a sustainable and profitable
export for New Zealand. Oyster farms are sensitive to
changes in salinity that can cause significant crop
loss if they persist too long. Recent extreme weather
events have been leading to increased periods of low
salinity, putting the farms at risk. Machine learning
based methods provide a way to predict these low
salinity events and provide an early warning system,
but this has not been investigated in aquaculture. In
this paper, we investigate three different methods to
assess the viability of salinity prediction systems. A
simple statistical model, a genetic programming (GP)
based symbolic regression model and a convolutional
neural network (CNN) were compared as ways of solving
this problem. The results show that GP based symbolic
regression and CNNs are fairly good approaches to
predicting salinity. However, as weather events get
more extreme, the CNN approach tends to hold up better
and can be generalised better, while the G P based
symbolic regression models show better potential
explainability with the tree based model structure.
These results show promise and provide a good stepping
off point at creating a generalised approach to
predicting salinity in estuaries.",
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notes = "also known as \cite{10612099}
WCCI 2024",
- }
Genetic Programming entries for
Matthew Harper
Ivy Liu
Bing Xue
Ross Vennell
Mengjie Zhang
Citations