Aquaphotomics Determination of Total Organic Carbon and Hydrogen Biomarkers on Aquaponic Pond Water and Concentration Prediction Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Concepcion:2020:HTC,

• author = "Ronnie {Concepcion II} and Sandy Lauguico and Jonnel Alejandrino and Justin {De Guia} and Elmer Dadios and Argel Bandala",
• title = "Aquaphotomics Determination of Total Organic Carbon and Hydrogen Biomarkers on Aquaponic Pond Water and Concentration Prediction Using Genetic Programming",
• booktitle = "2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC)",
• year = "2020",
• 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.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/R10-HTC49770.2020.9357030",
• ISSN = "2572-7621",
• month = dec,
• 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

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