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.

scholarly journals Modeling air quality index using optimized neuronal networks inspired by swarms

2020 ◽  
Vol 26 (6) ◽  
pp. 200469-0
Author(s):  
Dimple Pruthi ◽  
Rashmi Bhardwaj
Keyword(s):  
Air Quality ◽  
Fuzzy Inference System ◽  
Quality Index ◽  
Neuronal Networks ◽  
Fuzzy Inference ◽  
Air Pollutant ◽  
Air Quality Index ◽  
Inference System ◽  
Air Quality Prediction ◽  
Neuro Fuzzy

Air quality prediction is a significant field in environmental engineering, as air and water are essential for life on Earth. Nowadays, a common parameter used worldwide to measure air quality is termed as Air quality index. The parameter is measured based on the air pollutant concentration. The hybrid neuronal networks have been widely used for modeling air quality index. In the quest of optimizing the error in modeling air quality index, the existing adaptive neuro-fuzzy inference system is improved in this study using algorithms based on evolution and swarm movement. The model is based on the prominent air pollutants- nitrogen oxide, particulate matter of size equal to or less than 2.5microns (PM2.5), and sulphur dioxide. The proposed hybrid model using wavelet transform, particle swarm optimization, and adaptive neuro-fuzzy inference system accurately predicts the Air Quality Index and can be used in the public interest to take necessary precautions beforehand.

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.

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.

APPLICATION OF AIR QUALITY INDEX AND INVERSE DISTANCE WEIGHTING FOR MAPPING THE DISTRIBUTION OF AIR POLLUTION AT SEVERAL URBAN DISTRICTS OF HANOI CITY

2020 ◽  
Vol 65 (10) ◽  
pp. 189-200
Author(s):  
Khac Dang Vu ◽  
Anh Nguyen Thi Van
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Ambient Air ◽  
Inverse Distance Weighting ◽  
Air Quality Index ◽  
Pollution Level ◽  
Urban Districts ◽  
Distance Weighting ◽  
Inverse Distance

The air pollution level can be assessed using air quality index - AQI calculated from the concentration of some gases and particle matters which are measured at ambient air quality monitoring stations. The calculated AQI values are characterized by temporal continuity but spatial discontinuity. However, AQI values of each monitoring station is interpolated by the IDW (Inverse Distance Weighting) method in GIS which helps us to assess the air quality at a detailed and specific level for every location in the study area by establishing distribution maps of air pollution. The interpolation of AQI values for zoning air quality in several urban districts of Hanoi during the Winter (October, November, December 2019) shows that in general, the areas with a very bad level of air quality occupied an important surface in the Northwest of urban districts (on the territory of Bac Tu Liem, Ba Dinh, Tay Ho, Cau Giay) for last 3 months of the year. The areas with a bad level of air quality occupied a large surface in the Southeast in October and December, but its surface became narrow in November. But in November, areas having a bad level of air quality were expanded to the Southeast while they occupied only a small surface at the center of the study area in October and December. Although the distribution of each level vary in terms of coverage, their common pattern has been conserved during three months of Winter. The distribution map of air quality provides the complete picture of the air pollution situation and it helps to adequately evaluate this issue in the urban districts of Hanoi city.

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 Analysis of Ambient Air Quality Based On Exceedance Factor and Air Quality Index for Siliguri City, West Bengal

2020 ◽  
pp. 236-246 ◽  
Author(s):  
Subham Roy ◽  
Nimai Singha
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Ambient Air ◽  
Air Quality Index ◽  
National Standard ◽  
Pollution Level ◽  
Pm2.5 And Pm10 ◽  
The City ◽  
Exceedance Factor

Bad air is one of the key concerns for most of the urban centres today, and Siliguri is no exceptions to this. In order to assess the air quality of Siliguri, Exceedance factor (EF) method was applied based on the average annual concentration of the pollutants named as; NO2, SO2, PM2.5 and PM10 and it is found that PM2.5 and PM10 are the major pollutants that pose a severe threat for the city. After applying the EF method, it is found that the values of PM2.5 was between moderate to high pollution level and for PM10 it falls under high to critical pollution level. On the other hand, the concentration of NO2 and SO2 falls under moderate to low pollution level. Through trend analysis of the various pollutants, it is found that their concentration was varying in nature. In case of PM10, the trend shows high concentration which exceeds national standard; whereas PM2.5 shows its concentration near towards violating the national standard soon if not checked. In contrast, trends of NO2 and SO2 were recorded lower than the national standard. The present situation of ambient air of Siliguri was analyzed based on Air Quality Index which reveals that air quality of the city can be classified into two seasons, i.e. clean air period (from April to October) and polluted period (from November to March). Lastly, the annual trends of PM2.5 and PM10 were constructed as they are the major pollutants, and it shows their skewed nature during winter months which results in smog episodes. It unveils how critical the situation of air quality of Siliguri became especially during winter months which seek immediate attention. Thus the study tries to present a vivid scenario about the present air quality of Siliguri, which concludes with some of the suggestions to restrain the air quality.

scholarly journals Calibration Model of a Low-Cost Air Quality Sensor Using an Adaptive Neuro-Fuzzy Inference System

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4380 ◽  
Author(s):  
Kemal Alhasa ◽  
Mohd Mohd Nadzir ◽  
Popoola Olalekan ◽  
Mohd Latif ◽  
Yusri Yusup ◽  
...  
Keyword(s):  
Air Quality ◽  
Supervised Learning ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Low Cost ◽  
Anfis Model ◽  
Inference System ◽  
Reference Instrument ◽  
Neuro Fuzzy ◽  
And Performance

Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O3 measurements due to the lack of a reference instrument for CO and NO2. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO2) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.

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