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JACIII Vol.26 No.6 pp. 937-943
doi: 10.20965/jaciii.2022.p0937
(2022)

Paper:

Fuzzy Logic-Based Adaptive Aquaculture Water Monitoring System Based on Instantaneous Limnological Parameters

Mary Grace Ann C. Bautista*1,†, Maria Gemel B. Palconit*1, Marife A. Rosales*1, Ronnie S. Concepcion II*2, Argel A. Bandala*1, Elmer P. Dadios*2, and Bernardo Duarte*3,*4

*1Department of Electronics and Computer Engineering, De La Salle University
2401 Taft Avenue, Malate, Manila 1004, Philippines

*2Department of Manufacturing Engineering and Management, De La Salle University
2401 Taft Avenue, Malate, Manila 1004, Philippines

*3Marine and Environmental Sciences Centre & Aquatic Research Infrastructure Network Associated Laboratory,
Faculdade de Ciências da Universidade de Lisboa
Cidade Universitária, Alameda da Universidade, Lisboa 1749-016, Portugal

*4Departamento de Biologia Vegetal, Faculdade de Ciências da Universidade de Lisboa
Cidade Universitária, Alameda da Universidade, Lisboa 1749-016, Portugal

Corresponding author

Received:
April 10, 2022
Accepted:
June 15, 2022
Published:
November 20, 2022
Keywords:
aquaculture, fuzzy systems, limnological parameters
Abstract
Fuzzy Logic-Based Adaptive Aquaculture Water Monitoring System Based on Instantaneous Limnological Parameters

Aquaculture water monitoring system

Water quality is crucial for maintaining a sustainable living environment in aquaculture. Limnological parameters affects the fish physiology, growth rate, and feed efficiency and may lead to high mortality rate under extreme conditions. The development of an adaptive aquaculture monitoring system for water quality using fuzzy logic will address this problem. Using Mamdani-type fuzzy inferences system (FIS) model, the input limnological parameters such as pH, temperature, total dissolved solids, and dissolved oxygen levels were transformed to four output states: excellent, good, poor, and toxic, for the prediction of water quality. For the simulation and evaluation of the developed FIS, MATLAB Simulink was used. Results of this study can be integrated with a feedback system for appropriate treatments including filtering, aeration, and water flushing to maintain safe environment for Nile tilapia.

Cite this article as:
M. Bautista, M. Palconit, M. Rosales, R. II, A. Bandala, E. Dadios, and B. Duarte, “Fuzzy Logic-Based Adaptive Aquaculture Water Monitoring System Based on Instantaneous Limnological Parameters,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.6, pp. 937-943, 2022.
Data files:
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Last updated on Dec. 01, 2022