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Predicting Vehicular SO2 Emissions using Artificial Neural Networks and Mamdani Fuzzy Logic: A Comparative Analysis

by Nixon Deb Pritom, Tanmoy Goswami
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 37
Year of Publication: 2024
Authors: Nixon Deb Pritom, Tanmoy Goswami
10.5120/ijca2024923945

Nixon Deb Pritom, Tanmoy Goswami . Predicting Vehicular SO2 Emissions using Artificial Neural Networks and Mamdani Fuzzy Logic: A Comparative Analysis. International Journal of Computer Applications. 186, 37 ( Aug 2024), 27-36. DOI=10.5120/ijca2024923945

@article{ 10.5120/ijca2024923945,
author = { Nixon Deb Pritom, Tanmoy Goswami },
title = { Predicting Vehicular SO2 Emissions using Artificial Neural Networks and Mamdani Fuzzy Logic: A Comparative Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2024 },
volume = { 186 },
number = { 37 },
month = { Aug },
year = { 2024 },
issn = { 0975-8887 },
pages = { 27-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number37/predicting-vehicular-so2-emissions-using-artificial-neural-networks-and-mamdani-fuzzy-logic-a-comparative-analysis/ },
doi = { 10.5120/ijca2024923945 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-08-31T23:18:25+05:30
%A Nixon Deb Pritom
%A Tanmoy Goswami
%T Predicting Vehicular SO2 Emissions using Artificial Neural Networks and Mamdani Fuzzy Logic: A Comparative Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 37
%P 27-36
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Air pollution, particularly SO2 emission from vehicular sources due to rapid urbanization and mass migration towards city, poses significant environmental and health risks. This study aims to develop and evaluate predictive models for vehicular SO2 concentration using Artificial Neural Network (ANN) and Mamdani Fuzzy Logic. The hourly SO2 concentration in the study areas exceeds the 0.075 ppm, 1hr-SO2 standard set by the Environmental Protection Agency. In this investigation, an artificial neural network (ANN) was applied to forecast SO₂ concentrations based on the types and quantities of vehicles using a Multi-Layer Perceptron (MLP) architecture which was improved by backpropagation. A Mamdani Fuzzy Logic inference system was constructed and simulated using MATLAB. Six different vehicles were used as independent variables in this study to predict SO2 levels using trapezoidal and triangular membership function. The findings reveal that the Mamdani Fuzzy Logic model and the Artificial Neural Network (ANN) model can both accurately estimate the concentrations of SO₂ in vehicles, however, the ANN model performs better and provides more insightful information than the fuzzy logic model due to its human-like reasoning. The correlation coefficient between predicted and observed SO2 concentration in the neural network model is R=0.9294, with a root mean square error of 0.0267, the ANN model performed better than FLA model (RMSE = 0.04895). In the future, it will be necessary to incorporate meteorological variables to create the optimal model.

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Index Terms

Computer Science
Information Sciences

Keywords

Air Pollution Sulfur Dioxide Artificial Neural Networks Mamdani Fuzzy Logic Air Quality Prediction

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