In Situ Indirect Detection of Phosphate Concentration from Aquaculture Water Using Physico-limnological Sensor-Based Feed-Forward Neural Network
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @InProceedings{Mendigoria:2022:HNICEM,
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author = "Christan Hail Mendigoria and Ronnie Concepcion and
Maria Gemel Palconit and Heinrick Aquino and
Oliver John Alajas and Elmer Dadios and Argel Bandala",
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booktitle = "2022 IEEE 14th International Conference on Humanoid,
Nanotechnology, Information Technology, Communication
and Control, Environment, and Management (HNICEM)",
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title = "In Situ Indirect Detection of Phosphate Concentration
from Aquaculture Water Using Physico-limnological
Sensor-Based Feed-Forward Neural Network",
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year = "2022",
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abstract = "High phosphate levels in aquatic ecosystems induce
eutrophication, which causes algae to overgrow and
disrupt biodiversity. Determining the phosphate
concentration is significant for maintaining optimal
ecological function and water quality. In this study,
five machine learning regression techniques were
investigated for detecting the phosphate level of an
aquaculture environment. The computational models
employed physico-limnological data such as pH level,
electrical conductivity, and water temperature as
predictors, acquired from the tilapia fishpond in
Rizal, Philippines. These models were evaluated based
on the defined criteria such as the predictive
performance and mean absolute error. All machine
learning models produced acceptable results with R2
value greater than 0.8. Among these, the feed-forward
neural network model is concluded to be the most
effective phosphate prediction model with R2=0.93 and
MAE=0.39. Furthermore, a hybrid approach of multigene
genetic programming and genetic algorithm (MGGP-GA) was
implemented for optimisation of phosphate level.
GPTIPSv2, a MGGP tool, was used to create symbolic
models. The model with the highest predictive accuracy
and lower complexity was configured as the key
component of GA architecture. This optimisation
technique produced an optimum parameter value of
21.79degreeC for water temperature, 6.9 for pH, and
0.74 mS/cm for electrical conductivity with a fitness
value evaluation of 7.521. With that, this approach
serves as an effective technique for in situ managing
the nutrient content, particularly the phosphate level
of aquatic systems.",
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keywords = "genetic algorithms, genetic programming, Temperature
sensors, Temperature measurement, Biological system
modelling, Computational modelling, Machine learning,
Water quality, Predictive models, aquaculture, machine
learning, neural network, ANN, phosphate detection,
physico-limnological parameters",
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DOI = "doi:10.1109/HNICEM57413.2022.10109612",
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ISSN = "2770-0682",
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month = dec,
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notes = "Also known as \cite{10109612}",
- }
Genetic Programming entries for
Christan Hail Mendigoria
Ronnie S Concepcion II
Maria Gemel B Palconit
Heinrick L Aquino
Oliver John Y Alajas
Elmer Jose P Dadios
Argel A Bandala
Citations