Using Fuzzy Logic for Real - Time Water Quality Assessment Monitoring System

Bokingkito, Paul B.; Caparida, Lomesindo T.

doi:10.1145/3293688.3293695

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…This implication function will be used to find the 𝑍 value. The defuzzification value can be obtained by calculating the 𝑍 value using the equation (12),…”

Section: ) Development Of Decision Support Usingmentioning

confidence: 99%

Water quality assessment monitoring system using fuzzy logic and the internet of things

Fakhrurroja,

Nuryatno,

Munandar

et al. 2023

J. Mechatron. Electr. Power Veh. Technol.

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Water utilization has recently been at its highest level of demand. The water needed to be clean, healthy, and determined to be suitable for consumption. Therefore, it is necessary to have a system that can monitor the water quality so thatinformation related to wate r suitability can be received regularly and in real-time. This paper addresses the critical need for real-time water quality monitoring systems. This study proposed a novel approach integrating the Tsukamoto fuzzy algorithm into an internet of things (IoT)-based framework, forming part of the Fuzzy Inference System. Our system serves as a decision support tool, enabling continuous assessment of water quality. The method categorizes water quality into three levels: good, moderate, and unhealthy, providing timely and precise suitability information. The results demonstrate the effectiveness of the fuzzy logic method in delivering accurate output. Through remotely deployed IoT devices, water suitability and status can be monitored and analyzed in real-time over the internet. This research bridges the gap between traditional water quality assessment methods and the demands of our modern, technology-driven society, ensuring a reliable supply of safe and consumable water.

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“…This implication function will be used to find the 𝑍 value. The defuzzification value can be obtained by calculating the 𝑍 value using the equation (12),…”

Section: ) Development Of Decision Support Usingmentioning

confidence: 99%

Water quality assessment monitoring system using fuzzy logic and the internet of things

Fakhrurroja,

Nuryatno,

Munandar

et al. 2023

J. Mechatron. Electr. Power Veh. Technol.

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“…They only considered the DO and the water temperature. Bokingkito and caparida [20] implemented an IoT-based real -time water quality assessment monitoring system for freshwater aquaculture they considered temperature, pH and turbidity.…”

Section: Introductionmentioning

confidence: 99%

AquaStat: An Arduino-based Water Quality Monitoring Device for Fish Kill Prevention in Tilapia Aquaculture using Fuzzy Logic

Molato¹

2022

IJACSA

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In the Philippines, Tilapia fish farming sector is vital to the economy in providing substantial employment, income and meeting local demand for protein sources of the Filipinos. However, the possible benefits that can be derived from this industry are at stake because of the sudden occurrences of fish kill events. This can be attributed to a wide variety of natural and unnatural causes such as old age, starvation, body injury, stress, suffocation, water pollution, diseases, parasites, predation, toxic algae, severe weather, and other reasons. With the identified severe effects of fish kill events to the fish farmers, consumers and the fisheries industry, advanced measures and methods must be established to alleviate the adverse effects of this phenomenon. To solve the underlying problem on water quality monitoring system to improve freshwater aquaculture, various studies were already conducted. However, these studies merely focused on the reading and gathering of water parameters. In this paper, fuzzy logic was used to come up with a model that can analyze and generate result regarding the overall quality of the water being used in Tilapia aquaculture. The water parameters considered in this paper were temperature, dissolved oxygen, and pH level. The results of the water parameter readings using the conventional method were compared to the data that were gathered by AquaStat to test its accuracy and showed no significant difference. Also, the overall water quality obtained using the conventional method was compared to the overall water quality generated by AquaStat and obtained an accurate result.

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“…Fuzzy Logic is a branch of Artificial Intelligence, which is knowledge that makes computers able to imitate human intelligence [18]. Fuzzy logic can solve problems in stored controllers and information processing that can be implemented on embedded systems on microcontrollers [19]. This prototype can detect water turbidity by sending reports in real-time and displaying turbidity data in the form of numbers on the LCD.…”

Section: Introductionmentioning

confidence: 99%

Microcontroller Based Water Measurement Level Prototype Using Fuzzy Logic Method

Dalle¹,

Elfirman²,

Sufyan³

2020

TEM Journal

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Water is the source of life for humans. Its sustainability has to be maintained because water’s quality will affect human life. One of the water sources that are usually used by people is from the local water Company (PDAM) in every city in Indonesia. One of the PDAM Companies in the Marabahan City, South Kalimantan, is treating water from the Jejangkit River, but that water is not always clean; it can be murkier in a rainy season and tidal conditions. That is why the water quality condition in that river needs to be known. In that case, this study aims at designing a prototype of microcontroller-based turbidity of water detector by implementing the fuzzy logic. It can send a real-time report where later it will be used by PDAM in Marabahan City. The stages of turbidity measurement consist of start, initialization of I/O, turbidity sensor of reading object, fuzzy logic, read the turbidity level, stop. The result of this study is that the developed prototype is able to read the turbidity level of water and it can send a real-time report. It is recommended to develop the further turbidity sensor by using other components that are not only able to read the turbidity level, but they are also able to read the pH value and can be developed with other methods, in order that it can read the sharper data accuracy.

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Using Fuzzy Logic for Real - Time Water Quality Assessment Monitoring System

Cited by 8 publications

References 9 publications

Water quality assessment monitoring system using fuzzy logic and the internet of things

Water quality assessment monitoring system using fuzzy logic and the internet of things

AquaStat: An Arduino-based Water Quality Monitoring Device for Fish Kill Prevention in Tilapia Aquaculture using Fuzzy Logic

Microcontroller Based Water Measurement Level Prototype Using Fuzzy Logic Method

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