Multi-Gene Genetic Programming of IoT Water Quality Index Monitoring from Fuzzified Model for Oreochromis niloticus Recirculating Aquaculture System

Real-time water quality index (WQI) monitoring – a simplified single variable indication of water quality (WQ) – is vital in attaining a sustainable future in precision aquaculture. Although several monitoring systems for water quality parameters (WQP) use IoT, there is no existing WQI IoT monitorin...

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Bibliographic Details
Published in:Journal of advanced computational intelligence and intelligent informatics 2022-09, Vol.26 (5), p.816-823
Main Authors: Palconit, Maria Gemel B., Bautista, Mary Grace Ann C., II, Ronnie S. Concepcion, Alejandrino, Jonnel D., Evangelista, Ivan Roy S., Alajas, Oliver John Y., Vicerra, Ryan Rhay P., Bandala, Argel A., Dadios, Elmer P.
Format: Article
Language:English
Subjects:
Aquaculture
Computation
Dashboards
Fuzzy sets
Genetic algorithms
Monitoring
Neural networks
Nonlinear equations
Platforms
Ponds
Tilapia
Water quality
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Summary:Real-time water quality index (WQI) monitoring – a simplified single variable indication of water quality (WQ) – is vital in attaining a sustainable future in precision aquaculture. Although several monitoring systems for water quality parameters (WQP) use IoT, there is no existing WQI IoT monitoring for Oreochromis niloticus because the current WQI models are too complex to be deployed for low-level computing platforms such as the IoT modules and dashboards. Thus, the development of the IoT-based WQI fuzzy inference system (FIS) was simplified by the multi-gene genetic programming (MGGP) to search for non-linear equations given the simulated WQP fuzzy sets. Results have shown that the implemented novel system can accurately predict the WQI IoT monitoring with an average of R 2 and RMSE of 0.9112 and 0.6441, respectively. Implementing WQI in the IoT monitoring dashboard using the MGGP has significantly addressed the present challenges in deploying other complex AI-based models for WQI, such as the FIS and neural networks in low-computing capable platforms.
ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2022.p0816