Mamdani fuzzy inference system for mapping water quality level of biofloc ponds in catfish cultivation

The government has launched a program to increase the production of catfish by using biofloc ponds. The biofloc ponds can maintain the quality of water biologically to maximize the growth of fish. However, the level of water quality monitoring is generally only divided into good or bad categories so that it cannot represent the condition of fish growth. Therefore, this study aims to get the level of water quality (0–100 %) using the Mamdani fuzzy inference system (FIS) algorithm based on pH, temperature, and dissolved oxygen (DO). The level of water quality was correlated based on catfish growth conditions. The results showed that the range of values of the water quality level for each condition of catfish growth was 100 % for normal-living fish, 83–99 % for stunted fish growth, and < 83% for threatened fish. The FIS algorithm had 89.92 % of accuracy.

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Using a Mamdani Fuzzy Inference System Model (MFISM) for Ranking Groundwater Quality in an Agri-Environmental Context: Case of the Hammamet-Nabeul Shallow Aquifer (Tunisia)

Water ◽

10.3390/w13182507 ◽

2021 ◽

Vol 13 (18) ◽

pp. 2507

Author(s):

Soumaya Hajji ◽

Naima Yahyaoui ◽

Sonda Bousnina ◽

Fatma Ben Brahim ◽

Nabila Allouche ◽

...

Keyword(s):

Water Quality ◽

Groundwater Quality ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

System Model ◽

Shallow Aquifer ◽

Treated Wastewater ◽

Sodium Absorption ◽

Inference System ◽

Mamdani Fuzzy Inference System

Using an adaptive Mamdani fuzzy inference system model (MFSIM), the purpose of this paper is mainly to assess and rank the assessment and ranking of water quality for irrigation occurring in the Hammamet-Nabeul (Tunisia) shallow aquifer. This aquifer is under Mediterranean climate conditions and affected by intensive and irrational agricultural activities. In the current study, the Mamdani fuzzy logic-based decision-making approach was adapted to classify groundwater quality (GW) for irrigation. The operation of the fuzzy model is based on the input membership functions of electrical conductivity (EC) and sodium absorption ratio (SAR) and on the output membership function of the irrigation water quality index (IWQI). Validation of the applied MFISM showed a rate of about 80%. Therefore, MFISM was shown to be reliable and flexible in quality ranking for irrigation in an uncertain and complex hydrogeological system. The results demonstrated that water quality contamination in the aquifer is affected by the overlaying of three types of negative anthropogenic practices: the excess use of water for irrigation and chemical fertilizers, and the rejection of partially treated wastewater in some areas. The implemented approach led to identifying the spatial distribution of water quality for irrigation in the studied area. It is considered a helpful tool for water agri-environmental sustainability and management.

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The fuzzy inference system for intelligent water quality monitoring system to optimize eel fish farming

2016 International Symposium on Electronics and Smart Devices (ISESD) ◽

10.1109/isesd.2016.7886712 ◽

2016 ◽

Author(s):

Sri Wahjuni ◽

Ardhi Maarik ◽

Tatag Budiardi

Keyword(s):

Water Quality ◽

Monitoring System ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Fish Farming ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Inference System

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Comparative Analysis of Tsukamoto and Mamdani Fuzzy Inference System on Market Matching to Determine the Number of Exports for MSMEs

2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS) ◽

10.1109/eeccis.2018.8692989 ◽

2018 ◽

Author(s):

Elta Sonalitha ◽

Bambang Nurdewanto ◽

Salnan Ratih ◽

Nadia Roosmalita Sari ◽

Aries Boedi Setiawan ◽

...

Keyword(s):

Comparative Analysis ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Inference System ◽

Mamdani Fuzzy Inference System

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AN ALGORITHM OF FUZZY INFERENCE SYSTEM FOR ISAR IMAGE CLASSIFICATION

Environment Technology Resources Proceedings of the International Scientific and Practical Conference ◽

10.17770/etr2017vol2.2599 ◽

2017 ◽

Vol 2 ◽

pp. 154

Author(s):

Krasimir Slavyanov ◽

Chavdar Minchev

Keyword(s):

Neural Network ◽

Image Classification ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Information Analysis ◽

Simulation Experiments ◽

Inference System ◽

Classification Procedure ◽

Neural Network Simulation ◽

Mamdani Fuzzy Inference System

This article offers an original ISAR image classification procedure based on Mamdani fuzzy inference system (FIS) dedicated to compute multiple results each from different type of analyzing criteria. The modeling and information analysis of the FIS are developed to draw a general conclusion from several results each produced by classification from neural network. Simulation experiments are carried out in MATLAB environment.

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Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

Computers Materials & Continua ◽

10.32604/cmc.2022.019836 ◽

2022 ◽

Vol 71 (2) ◽

pp. 3019-3033

Author(s):

Tahir Alyas ◽

Iqra Javed ◽

Abdallah Namoun ◽

Ali Tufail ◽

Sami Alshmrany ◽

...

Keyword(s):

Fuzzy Inference System ◽

Virtual Machines ◽

Fuzzy Inference ◽

Live Migration ◽

Inference System ◽

Mamdani Fuzzy Inference System

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Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system

Journal of Hydroinformatics ◽

10.2166/hydro.2011.084 ◽

2011 ◽

Vol 14 (1) ◽

pp. 167-179 ◽

Cited By ~ 19

Author(s):

Vesna Ranković ◽

Jasna Radulović ◽

Ivana Radojević ◽

Aleksandar Ostojić ◽

Ljiljana Čomić

Keyword(s):

Experimental Data ◽

Water Quality ◽

Dissolved Oxygen ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Adaptive Network ◽

Inference System ◽

Fuzzy Models ◽

Highly Nonlinear ◽

Input Variables

Predicting water quality is the key factor in the water quality management of reservoirs. Since a large number of factors affect the water quality, traditional data processing methods are no longer good enough for solving the problem. The dissolved oxygen (DO) level is a measure of the health of the aquatic system and its prediction is very important. DO dynamics are highly nonlinear and artificial intelligence techniques are capable of modelling this complex system. The objective of this study was to develop an adaptive network-based fuzzy inference system (ANFIS) to predict the DO in the Gruža Reservoir, Serbia. The fuzzy model was developed using experimental data which were collected during a 3-year period. The input variables analysed in this paper are: water pH, water temperature, total phosphate, nitrites, ammonia, iron, manganese and electrical conductivity. The selection of an appropriate set of input variables is based on the building of ANFIS models for each possible combination of input variables. Results of fuzzy models are compared with measured data on the basis of correlation coefficient, mean absolute error and mean square error. Comparing the predicted values by ANFIS with the experimental data indicates that fuzzy models provide accurate results.

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