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Daily SO2 Air Pollution Prediction with the use of Artificial Neural Network Models

by Yasemin Gültepe, Ayşe Mine Duru
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 34
Year of Publication: 2018
Authors: Yasemin Gültepe, Ayşe Mine Duru
10.5120/ijca2018918271

Yasemin Gültepe, Ayşe Mine Duru . Daily SO2 Air Pollution Prediction with the use of Artificial Neural Network Models. International Journal of Computer Applications. 181, 34 ( Dec 2018), 36-40. DOI=10.5120/ijca2018918271

@article{ 10.5120/ijca2018918271,
author = { Yasemin Gültepe, Ayşe Mine Duru },
title = { Daily SO2 Air Pollution Prediction with the use of Artificial Neural Network Models },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2018 },
volume = { 181 },
number = { 34 },
month = { Dec },
year = { 2018 },
issn = { 0975-8887 },
pages = { 36-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number34/30213-2018918271/ },
doi = { 10.5120/ijca2018918271 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:08:09.605721+05:30
%A Yasemin Gültepe
%A Ayşe Mine Duru
%T Daily SO2 Air Pollution Prediction with the use of Artificial Neural Network Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 34
%P 36-40
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Although Artificial Neural Networks have been used for many years, its use in air pollution analysis has become widespread in 10 years. In this study, an Artificial Neural Network model was proposed to predication air pollution in Kastamonu province of Turkey. In this study as an example of Kastamonu province, Artificial Neural Network model was formed by using a pollution parameter (PM10) and 5 different meteorological factors (air temperature, air pressure, humidity, wind direction and wind speed) which were measured daily data during (2015-2018) period. It is aimed to propose refined model to predict the value of air pollution concentration (SO2) after 24 hours by using this model. In other words, the air quality model is predicted. Artificial Neural Network is very successful compared to the new and classical statistical methods. Feed back-propagation algorithm has been used in all developed Artificial Neural Network model. The data set used in this study is divided into three subsets including training, validations and test data sets. The first 70% percent of the data set were used as the training subset, 15% of the data set used as the test set and 15% of the data set were used in validation set. The Mean Squeare Error (MSE) was measured for the performance of the network. It was observed that the developed ANN model was in agreement with the experimental results.

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Index Terms

Computer Science
Information Sciences

Keywords

Artifical neural networks feed back-propagation algorithm air pollution Kastamonu.

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