DOI : https://doi.org/10.18517/ijaseit.12.5.15996

Water Quality Monitoring System Based on Fuzzy Algorithm

Mutiara Andara (1), Eko Purwanto (2), Awang Hendrianto Pratomo (3), Rudi Kurniawan (4), Hendri Himawan Triharminto (5)
(1) Department of Electrical Engineering, Indonesian Air Force Academy, Yogyakarta, Indonesia
(2) Department of Electrical Engineering, Indonesian Air Force Academy, Yogyakarta, Indonesia
(3) Department of Electrical Engineering, Indonesian Air Force Academy, Yogyakarta, Indonesia
(4) Department of Electrical Engineering, Indonesian Air Force Academy, Yogyakarta, Indonesia
(5) Department of Informatics, Faculty of Industrial Engineering, Universitas Pembangunan Nasional "Veteran" Yogyakarta, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
Andara, Mutiara, et al. “Water Quality Monitoring System Based on Fuzzy Algorithm”. International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 5, Oct. 2022, pp. 2105-11, doi:10.18517/ijaseit.12.5.15996.
Citation Format :
Water is one of the essential things in our life, especially for daily needs such as cooking, bathing, drinking, etc. Therefore, water quality is vital to keep humans stay healthy. The water fountain is one of the sources of water. This research designs a water monitoring device to determine the feasibility level of water produced in a water fountain using two sensors. The device measures water acidity using a pH sensor and water turbidity using a turbidity sensor. Both data measurements, water acidity, and turbidity, are processed on the Arduino nano microcontroller based on a fuzzy algorithm. The proposed fuzzy algorithm uses the Tsukamoto Fuzzy model method. The fuzzy inference system is implemented for each sensor to control the valve. There are three membership functions for the fuzzy set of pH and turbidity sensors. The model determines nine fuzzy rules that affect the solenoid valve. The solenoid valve will open if the water condition is suitable for drinking and close if the water condition is not suitable for drinking. The experimental setup is conducted for each pH and turbidity sensor. The turbidity sensor experiment used three water conditions, i.e., clean water, slightly cloud water, and dirty water. Differently, pH sensor considers three water conditions, i.e., acid, neutral, and alkaline water. Based on the result, the fuzzy inference system is determined and generates nine rules for solenoid valves as a defuzzification process. The result shows that implementing a fuzzy algorithm can be used as a filter to detect water conditions.

F. Torres-Bejarano, F. Arteaga-Herní¡ndez, D. Rodrí­guez-Ibarra, D. Mejí­a-ívila, and L. C. Gonzí¡lez-Mí¡rquez, “Water quality assessment in a wetland complex using Sentinel 2 satellite images,” International Journal of Environmental Science and Technology, vol. 18, no. 8, pp. 2345-2356, 2021, doi: 10.1007/s13762-020-02988-3.

J. Holloway and K. Mengersen, “Statistical machine learning methods and remote sensing for sustainable development goals: A review,” Remote Sensing, vol. 10, no. 9. MDPI AG, Sep. 01, 2018. doi: 10.3390/rs10091365.

M. Pule, A. Yahya, and J. Chuma, “Wireless sensor networks: A survey on monitoring water quality,” Journal of Applied Research and Technology, vol. 15, no. 6, pp. 562-570, 2017, doi: 10.1016/j.jart.2017.07.004.

N. A. Cloete, R. Malekian, and L. Nair, “Design of Smart Sensors for Real-Time Water Quality Monitoring,” IEEE Access, vol. 4, pp. 3975-3990, 2016, doi: 10.1109/ACCESS.2016.2592958.

K. Saravanan, E. Anusuya, R. Kumar, and L. H. Son, “Real-time water quality monitoring using Internet of Things in SCADA,” Environmental Monitoring and Assessment, vol. 190, no. 9, p. 556, 2018, doi: 10.1007/s10661-018-6914-x.

Y. Chen and D. Han, “Water quality monitoring in smart city: A pilot project,” Automation in Construction, vol. 89, pp. 307-316, 2018, doi: 10.1016/j.autcon.2018.02.008.

F. Heng, “Construction and research of water quality monitoring system based on ZigBee technology,” E3S Web of Conferences, vol. 165, 2020, doi: 10.1051/e3sconf/202016503060.

L. Parra, J. Rocher, J. Escriví¡, and J. Lloret, “Design and development of low cost smart turbidity sensor for water quality monitoring in fish farms,” Aquacultural Engineering, vol. 81, pp. 10-18, 2018, doi: https://doi.org/10.1016/j.aquaeng.2018.01.004.

I. Yaroshenko et al., “Real-time water quality monitoring with chemical sensors,” Sensors (Switzerland), vol. 20, no. 12, pp. 1-22, 2020, doi: 10.3390/s20123432.

X. Su, L. Sutarlie, and X. J. Loh, “Sensors, Biosensors, and Analytical Technologies for Aquaculture Water Quality,” research, vol. 2020, pp. 1-15, 2020, doi: 10.34133/2020/8272705.

C. Prudkin-Silva et al., “A cost-effective algae-based biosensor for water quality analysis: Development and testing in collaboration with peasant communities,” Environmental Technology and Innovation, vol. 22, pp. 1-13, 2021, doi: 10.1016/j.eti.2021.101479.

A. A. Nadiri, S. Shokri, F. T. C. Tsai, and A. Asghari Moghaddam, “Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model,” Journal of Cleaner Production, vol. 180, no. January, pp. 539-549, 2018, doi: 10.1016/j.jclepro.2018.01.139.

D. Pandey, S. Mishra, and S. Pandey, “Intelligent Control System for Water Pollutant Monitoring Using ANN and Fuzzy Logic,” in Intelligent Algorithms for Analysis and Control of Dynamical Systems, 2021, pp. 89-101. doi: 10.1007/978-981-15-8045-1_10.

M. Sakizadeh, “Artificial intelligence for the prediction of water quality index in groundwater systems,” Modeling Earth Systems and Environment, vol. 2, no. 1, pp. 1-9, 2016, doi: 10.1007/s40808-015-0063-9.

L. Arismendy, C. Cí¡rdenas, D. Gómez, A. Maturana, R. Mejí­a, and C. G. Quintero M., “Intelligent system for the predictive analysis of an industrial wastewater treatment process,” Sustainability (Switzerland), vol. 12, no. 16, 2020, doi: 10.3390/SU12166348.

A. Yalcuk and S. Postalcioglu, “Evaluation of pool water quality of trout farms by fuzzy logic: monitoring of pool water quality for trout farms,” International Journal of Environmental Science and Technology, vol. 12, no. 5, pp. 1503-1514, 2015, doi: 10.1007/s13762-014-0536-9.

S. Mazhar, A. Ditta, L. Bulgariu, I. Ahmad, M. Ahmed, and A. A. Nadiri, “Sequential treatment of paper and pulp industrial wastewater: Prediction of water quality parameters by Mamdani Fuzzy Logic model and phytotoxicity assessment,” Chemosphere, vol. 227, pp. 256-268, 2019, doi: 10.1016/j.chemosphere.2019.04.022.

J. O. Oladipo, A. S. Akinwumiju, O. S. Aboyeji, and A. A. Adelodun, “Comparison between fuzzy logic and water quality index methods: A case of water quality assessment in Ikare community, Southwestern Nigeria,” Environmental Challenges, vol. 3, p. 100038, 2021, doi: https://doi.org/10.1016/j.envc.2021.100038.

X. Q. Jiang, W. F. Chen, L. J. Guo, and Z. W. Huang, “Application of T-S fuzzy-neural network model in water quality comprehensive evaluation,” Procedia Computer Science, vol. 166, pp. 501-506, 2020, doi: 10.1016/j.procs.2020.02.057.

R. A. Bórquez López, L. R. Martinez Cordova, J. C. Gil Nuñez, J. R. Gonzalez Galaviz, J. C. Ibarra Gamez, and R. C. Hernandez, “Implementation and evaluation of open-source hardware to monitor water quality in precision aquaculture,” Sensors (Switzerland), vol. 20, no. 21, pp. 1-14, 2020, doi: 10.3390/s20216112.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).