JACIII Vol.26 No.5 pp. 816-823
doi: 10.20965/jaciii.2022.p0816


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

Maria Gemel B. Palconit*,**,†, Mary Grace Ann C. Bautista*, Ronnie S. Concepcion II***, Jonnel D. Alejandrino*, Ivan Roy S. Evangelista*, Oliver John Y. Alajas*, Ryan Rhay P. Vicerra***, Argel A. Bandala*, and Elmer P. Dadios***

*Department of Electronics and Computer Engineering, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

**Department of Electronics Engineering, Cebu Technological University
M. J. Cuenco Avenue, Cor R. Palma Street, 6000 Cebu, Philippines

***Department of Manufacturing Engineering and Management, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

Corresponding author

April 22, 2022
July 10, 2022
September 20, 2022
water quality index, Internet of Things, fuzzy systems, multi-gene genetic programming, precision aquaculture
Multi-Gene Genetic Programming of IoT Water Quality Index Monitoring from Fuzzified Model for <i><b>Oreochromis niloticus</b></i> Recirculating Aquaculture System

WQ data and WQI using FIS and MGGP

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 R2 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.

Cite this article as:
M. Palconit, M. Bautista, R. II, J. Alejandrino, I. Evangelista, O. Alajas, R. Vicerra, A. Bandala, and E. Dadios, “Multi-Gene Genetic Programming of IoT Water Quality Index Monitoring from Fuzzified Model for Oreochromis niloticus Recirculating Aquaculture System,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.5, pp. 816-823, 2022.
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Last updated on Sep. 22, 2022