Aquaphotomics Determination of Total Organic Carbon and Hydrogen Biomarkers on Aquaponic Pond Water and Concentration Prediction Using Genetic Programming
Created by W.Langdon from
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- @InProceedings{Concepcion:2020:HTC,
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author = "Ronnie {Concepcion II} and Sandy Lauguico and
Jonnel Alejandrino and Justin {De Guia} and Elmer Dadios and
Argel Bandala",
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title = "Aquaphotomics Determination of Total Organic Carbon
and Hydrogen Biomarkers on Aquaponic Pond Water and
Concentration Prediction Using Genetic Programming",
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booktitle = "2020 IEEE 8th R10 Humanitarian Technology Conference
(R10-HTC)",
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year = "2020",
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abstract = "Crops that are cultivated in aquaponics setup highly
relies on the nutrients supplied by the aqueous system
through fish effluents. Continuous monitoring of
essential elemental nutrients requires expensive
sensors and arrays of it for full scale deployment.
However, sustainable agriculture demands energy
consumption reduction and cost-effectiveness. This
study employed device minimization by using a
combination of physical water sensors, namely
temperature and electrical conductivity sensors, to
predict total organic carbon (TOC) and hydrogen ion (H)
concentrations in pond water. Aquaphotomics through
ultraviolet (UV) and visible light (Vis) wavelength
sweeping from 250 to 500 nm was explored to determine
the nutrient biomarkers of pond water samples that
undergoes temperature perturbation from 16 to 36
degreeC with 2 degreeC increment per testing. Principal
component analysis (PCA) selected the most relevant
activated water bands which are 275 nm for TOC and 415
nm for H. Direct spectrophotometric TOC concentration
data was passed through a Savitzky-Golay filter to
smoothen the nutrient signal. Recurrent neural network
(RNN) exhibited the fastest inference time of 3.5
seconds on the average with R2 of 0.8583 and 0.9686 for
predicting TOC and H concentrations. Multigene symbolic
regression genetic programming (MSRGP) exhibited the
best R2 performances of 0.9280 and 0.9693 in predicting
TOC and H concentrations by using only the temperature
and electrical conductivity sensoracquired data. This
developed model is an innovative approach on measuring
chemical concentrations of water using physical
limnological sensors which resulted to energy
consumption reduction of 50percent for complete 42-day
crop life cycle of lettuce.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/R10-HTC49770.2020.9357030",
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ISSN = "2572-7621",
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month = dec,
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notes = "Also known as \cite{9357030}",
- }
Genetic Programming entries for
Ronnie S Concepcion II
Sandy Lauguico
Jonnel D Alejandrino
Justin De Guia
Elmer Jose P Dadios
Argel A Bandala
Citations