A Fuzzy Inference System for Water Quality of Chunnambar River, Puducherry

2015 ◽  
Vol 787 ◽  
pp. 322-326 ◽  
Author(s):  
V. Nirmala ◽  
K.R. Leelavathy ◽  
Sivapragasam Sowndharya ◽  
Parthiban Bama
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Fuzzy Inference System ◽  
Quality Index ◽  
Fuzzy Inference ◽  
Oxygen Demand ◽  
Pollution Level ◽  
Inference System ◽  
Indian Water ◽  
Proposed Model

A Fuzzy Inference System (FIS) is considered as an effective tool for solution of many complex engineering systems when ambiguity and uncertainty is associated with the systems. The water quality is an important issue of relevance in the context of present times. The proposed model is designed to predict Water Quality Index (WQI) for Chunnambar, Ariyankuppam, Puducherry Region, Southern India. A systematic investigation of the pollution level at Chunnambar from March 2013 to February 2014 was carried out. The untreated domestic wastes from various parts of the Ariyankuppam town are directly discharged into the river which leads to increased level of pollution. The present studies emphasis on the magnitude of pollution by monitoring key water quality parameters such as Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), pH and Temperature. FIS simplifies and speed up the computation of WQI as compared to the currently existing standards. In this paper, the proposed model is compared with Indian Water Quality Index (IWQI) and it is found that the designed model predicts accurately.

Prediction of Water Quality Index Using Neuro Fuzzy Inference System

2011 ◽  
Vol 3 (3-4) ◽  
pp. 175-191 ◽  
Author(s):  
Mrutyunjaya Sahu ◽  
S. S. Mahapatra ◽  
H. B. Sahu ◽  
R. K. Patel
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Fuzzy Inference System ◽  
Quality Index ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals Evaluation of treated municipal wastewater effluent for agricultural irrigation purposes using the fuzzy effluent quality index (FEQI)

2019 ◽  
Vol 20 (1) ◽  
pp. 148-156
Author(s):  
Seyed Hesam Alihosseini ◽  
Ali Torabian ◽  
Farzam Babaei Semiromi
Keyword(s):  
Fuzzy Inference System ◽  
Quality Index ◽  
Municipal Wastewater ◽  
Fuzzy Inference ◽  
Treatment Plant ◽  
Wastewater Effluent ◽  
Inference System ◽  
Effluent Quality ◽  
Proposed Model ◽  
Treated Municipal Wastewater

Abstract The issues of freshwater scarcity in arid and semi-arid areas could be reduced via treated municipal wastewater effluent (TMWE). Artificial intelligence methods, especially the fuzzy inference system, have proven their ability in TMWE quality evaluation in complex and uncertain systems. The primary aim of this study was to use a Mamdani fuzzy inference system to present an index for agricultural application based on the Iranian water quality index (IWQI). Since the uncertainties were disregarded in the conventional IWQI, the present study improved this procedure by using fuzzy logic and then the fuzzy effluent quality index (FEQI) was proposed as a hybrid fuzzy-based index. TMWE samples of the Gheitarie wastewater treatment plant in Tehran city recorded from 2011 to 2017 were taken into consideration for testing the ability of the proposed index. The results of the FEQI showed samples categorized as ‘Excellent’ (21), ‘Good’ (10), ‘Fair’ (4), and ‘Marginal’ (1) for the warm seasons, and for the cool seasons, the samples categorized as ‘Excellent’, ‘Good’ and ‘Fair’ were 17, 18 and 1, respectively. Generally, a comparison between the IWQI and proposed model results revealed the FEQI's superiority in TMWE quality assessment.

scholarly journals Application of Fuzzy Inference System in the Prediction of Air Quality Index

2021 ◽  
Vol 6 (3) ◽  
pp. 75-85
Author(s):  
Nor Hayati Shafii ◽  
Nur Aini Mohd Ramle ◽  
Rohana Alias ◽  
Diana Sirmayunie Md Nasir ◽  
Nur Fatihah Fauzi
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Fuzzy Inference System ◽  
Quality Index ◽  
Fuzzy Inference ◽  
Average Concentration ◽  
Air Quality Index ◽  
Inference System ◽  
Proposed Model ◽  
The Impact

Air pollution is the presence of substances in the atmosphere that are harmful to the health of humans and other living beings. It is caused by solid and liquid particles and certain gases that are suspended in the air.  The air pollution index (API) or also known as air quality index (AQI) is an indicator for the air quality status at any area.  It is commonly used to report the level of severity of air pollution to public and to identify the poor air quality zone.  The AQI value is calculated based on average concentration of air pollutants such as Particulate Matter 10 (PM10), Ozone (O3), Carbon Dioxide (CO2), Sulfur Dioxide (SO2) and Nitrogen Dioxide (NO2).  Predicting the value of AQI accurately is crucial to minimize the impact of air pollution on environment and human health.  The work presented here proposes a model to predict the AQI value using fuzzy inference system (FIS). FIS is the most well-known application of fuzzy logic and has been successfully applied in many fields.  This method is proposed as the perfect technique for dealing with environmental well known and tackling the choice made below uncertainty.  There are five levels or indicators of AQI, namely good, moderate, unhealthy, very unhealthy, and hazardous. This measurement is based on classification made from the Department of Environment (DOE) under the Ministry of Science, Technology, and Innovation (MOSTI). The results obtained from the actual data are compared with the results from the proposed model.  With the accuracy rate of 93%, it shows that the proposed model is meeting the highest standard of accuracy in forecasting the AQI value.

scholarly journals Assessment study of water quality index (WQI) for Shatt Al-arab River and its branches, Iraq

2018 ◽  
Vol 162 ◽  
pp. 05005 ◽  
Author(s):  
Ahmed Hamdan ◽  
Ammar Dawood ◽  
Douaa Naeem
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Large Scale ◽  
Industrial Effluent ◽  
Oxygen Demand ◽  
River Pollution ◽  
Sewage Water ◽  
Quality Parameters ◽  
Pollution Level

Shatt Al-Arab River (in Basrah province South of Iraq) is approximately 192 km long. It plays a key role in providing water for domestic purposes, irrigation, manufacturing, in addition to shipment. Recently the river suffers from increasing pollution, due to wastes from industries, domestic sewage and agricultural activities that find their way into water sources and result in large scale deterioration of water quality. Investigating the river size and significance, becomes necessary to perform a study to understand the water quality of this river that is considered by some experts as one of the most contaminated in Iraq. This work uses the Water Quality Index (WQI) to describe the pollution level of the river and by using Geographic Information System (GIS) to create WQI map. This study also determines the critical pollutants affecting the river water quality throughout its course. WQI has been formulated making use of several water quality parameters such as pH, temperature, Dissolved Oxygen (DO), Biological Oxygen Demand（BOD5), Chemical Oxygen Demand（COD）, Nitrate (NO3-2), Phosphate (PO4-3), Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Turbidity (Tur), and Electrical Conductivity (E.C) which were measured at 37 sites along the river. Bad water quality was observed at the sites of the river branches, near the center of Basrah governorate. Furthermore, it was discovered that the main reason for river pollution was due to the high sewage water discharged into the river, especially river branches and illegal discharges of industrial effluent and sewage.

scholarly journals Assessment the Quality of Bottled Drinking Water Through Mamdani Fuzzy Water Quality Index

2021 ◽  
Author(s):  
Ghorban Asgari ◽  
Ensieh Komijani ◽  
Abdolmotaleb Seid-Mohammadi ◽  
Mohammad Khazaei
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Fuzzy Inference ◽  
Quality Parameters ◽  
Monte Carlo Algorithm ◽  
Retail Market ◽  
Inference System ◽  
Drinking Waters

Abstract In this investigation, an innovative index was developed based on the fuzzy inference system for assessing the quality of bottled drinking waters. A method was developed to aggregate the values obtained from the defuzzification step. A total number of 24 quality parameters revealing the characteristics of bottled were in terms of physiochemical, dietary, toxic, and pathogenic aspects were selected as the input parameters. 30 samples were taken from the independent brands found in the Hamadan province retail market to evaluate the bottled water quality index (BWQI). Results show that the values obtained from measuring the parameters are in the range of the standard levels set by national regulations. The BWQI scores obtained from samples were in the range of 61.2–73.8 attributing to the marginal and fair descriptive classes. Sensitivity analysis using the Monte Carlo algorithm reveals that the parameters NO3, Na, hardness, and NO2 have the most impact on the BWQI scores.

scholarly journals Forecasting of outdoor air quality index using adaptive Neuro Fuzzy Inference System

2022 ◽  
Author(s):  
Haripriyan Uthayakumar ◽  
Perarasu Thangavelu ◽  
Saravanathamizhan Ramanujam
Keyword(s):  
Air Quality ◽  
Fuzzy Inference System ◽  
Quality Index ◽  
Fuzzy Inference ◽  
Ambient Air ◽  
Air Quality Index ◽  
Quality Data ◽  
Pollution Level ◽  
Inference System ◽  
Neuro Fuzzy

Introduction: The estimation of air pollution level is well indicated by Air Quality Index (AQI), which tells how unhealthy the ambient air is and how polluted it can become in near future. Hence, the predictions or modeling of AQI is always of greater concern among researchers and this present study aims to develop such a model for forecasting the AQI. Materials and methods: A combination of Artificial Neural Network (ANN) and Fuzzy logic (FL) system, called Adaptive Neuro-Fuzzy Inference System (ANFIS) have been considered for model development. Daily air quality data (PM2.5 and PM10) and meteorological data (temperature and humidity) over a period of March 2020 to March 2021 were used as the input data and AQI as the output variable for the ANFIS model. The performances of models were evaluated based on Root Mean Square Error (RMSE), Regression coefficient (R2) and Average Absolute Relative Deviation (AARD). Results: A total of 100 datasets is split into training (70), testing (15) and simulation (15). Gaussian and Constant membership functions were employed for classifications and the final index consisted of 81 inference (IF/THEN) rules. The ANFIS Simulation result shows an R2 and RMSE value of 0.9872 and 0.0287 respectively. Conclusion: According to the results from this study, ANFIS based AQI is a comprehensive tool for classification of air quality and it is inclined to produce accurate results. Therefore, local authorities in air quality assessment and management schemes can apply these reliable and suitable results.

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