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

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.

WQ data and WQI using FIS and MGGP

WQ data and WQI using FIS and MGGP

Cite this article as:
M. Palconit, M. Bautista, R. Concepcion 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.
Data files:
  1. [1] F. Antonucci and C. Costa, “Precision aquaculture: a short review on engineering innovations,” Aquaculture Int., Vol.28, pp. 41-57, doi: 10.1007/s10499-019-00443-w, 2020.
  2. [2] M. G. B. Palconit et al., “Counting of Uneaten Floating Feed Pellets in Water Surface Images Using ACF Detector and Sobel Edge Operator,” IEEE 9th Region 10 Humanitarian Technology Conf. (R10-HTC), doi: 10.1109/R10-HTC53172.2021.9641579, 2021.
  3. [3] M. Abdelsalam et al., “Coinfections of Aeromonas spp., Enterococcus faecalis, and Vibrio alginolyticus isolated from farmed Nile tilapia and African catfish in Egypt, with an emphasis on poor water quality,” Microbial Pathogenesis, Vol.160, doi: 10.1016/j.micpath.2021.105213, 2021.
  4. [4] L. Parra et al., “Physical Sensors for Precision Aquaculture: A Review,” IEEE Sensors J., Vol.18, No.10, pp. 3915-3923, doi: 10.1109/JSEN.2018.2817158, 2018.
  5. [5] S. Gupta and S. K. Gupta, “A critical review on water quality index tool: Genesis, evolution and future directions,” Ecological Informatics, Vol.63, doi: 10.1016/J.ECOINF.2021.101299, 2021.
  6. [6] S. B. H. S. Asadollah et al., “River water quality index prediction and uncertainty analysis: A comparative study of machine learning models,” J. of Environmental Chemical Engineering, Vol.9, No.1, doi: 10.1016/J.JECE.2020.104599, 2021.
  7. [7] M. G. Uddin, S. Nash, and A. I. Olbert, “A review of water quality index models and their use for assessing surface water quality,” Ecological Indicators, Vol.122, doi: 10.1016/J.ECOLIND.2020.107218, 2021.
  8. [8] M. Kachroud et al., “Water Quality Indices: Challenges and Application Limits in the Literature,” Water, Vol.11, No.2, doi: 10.3390/w11020361, 2019.
  9. [9] M. G. B. Palconit et al., “Development of IoT-based Fish Tank Monitoring System,” IEEE 13th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), doi: 10.1109/HNICEM54116.2021.9731950, 2021.
  10. [10] D. R. Prapti et al., “Internet of Things (IoT)-based aquaculture: An overview of IoT application on water quality monitoring,” Reviews in Aquaculture, Vol.14, No.2, pp. 979-992, doi: 10.1111/raq.12637, 2022.
  11. [11] R. A. G. Parilla et al., “Low-cost garbage level monitoring system in drainages using internet of things in the Philippines,” Mindanao J. of Science and Technology, Vol.18, No.1, pp. 164-186, 2020.
  12. [12] M. G. B. Palconit and W. A. Nunez, “CO2 emission monitoring and evaluation of public utility vehicles based on road grade and driving patterns: An Internet of Things application,” IEEE 9th Int. Conf. on HNICEM, doi: 10.1109/HNICEM.2017.8269496, 2017.
  13. [13] M. G. B. Palconit and W. A. Nunez, “Statistical analysis of CO2 emission based on road grade, acceleration and vehicle specific power for public utility vehicles: An IoT application,” IEEE 4th World Forum on Internet of Things (WF-IoT), doi: 10.1109/WF-IoT.2018.8355235, 2018.
  14. [14] M. G. B. Palconit et al., “Speech Activation for Internet of Things Security System in Public Utility Vehicles and Taxicabs,” IEEE 11th Int. Conf. on HNICEM, doi: 10.1109/HNICEM48295.2019.9073370, 2019.
  15. [15] J. Alejandrino et al., “A Hybrid Data Acquisition Model Using Artificial Intelligence and IoT Messaging Protocol for Precision Farming,” IEEE 12th Int. Conf. on HNICEM, doi: 10.1109/HNICEM51456.2020.9400152, 2020.
  16. [16] M. G. B. Palconit et al., “Adaptive compensator of magnetic levitation system using symbolic regression,” IEEE Region 10 Annual Int. Conf. (TENCON), doi: 10.1109/TENCON50793.2020.9293857, 2020.
  17. [17] M. G. Palconit et al., “FishEye: A Centroid-Based Stereo Vision Fish Tracking Using Multigene Genetic Programming,” IEEE 9th R10-HTC, doi: 10.1109/R10-HTC53172.2021.9641654, 2021.
  18. [18] J.-A. V. Magsumbol et al., “Multigene Genetic Programming Model for Temperature Optimization to Improve Lettuce Quality,” IEEE 13th Int. Conf. on HNICEM, doi: 10.1109/HNICEM54116.2021.9731974, 2021.
  19. [19] R. S. Concepcion II, E. P. Dadios, and J. Cuello, “Non-destructive in situ measurement of aquaponic lettuce leaf photosynthetic pigments and nutrient concentration using hybrid genetic programming,” Agrivita, Vol.43, No.3, pp. 589-610, doi: 10.17503/agrivita.v43i3.2961, 2021.
  20. [20] C. H. R. Mendigoria et al., “Varietal Classification of Lactuca Sativa Seeds Using an Adaptive Neuro-Fuzzy Inference System Based on Morphological Phenes,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.5, pp. 618-624, doi: 10.20965/jaciii.2021.p0618, 2021.
  21. [21] H. Y. Yildiz et al., “Fish welfare in aquaponic systems: Its relation to water quality with an emphasis on feed and faeces – A review,” Water, Vol.9, No.1, doi: 10.3390/w9010013, 2017.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 03, 2024