Air pollution prediction using semi-experimental regression model and Adaptive Neuro-Fuzzy Inference System
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F20%3APU137312" target="_blank" >RIV/00216305:26210/20:PU137312 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0959652620312658?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0959652620312658?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jclepro.2020.121218" target="_blank" >10.1016/j.jclepro.2020.121218</a>
Alternative languages
Result language
angličtina
Original language name
Air pollution prediction using semi-experimental regression model and Adaptive Neuro-Fuzzy Inference System
Original language description
Lifestyle development and increasing urbanisation and consumption of fossil fuels, monitoring and controlling air pollution have become more important. This study has used the available data of key pollutants to predict their future status through time-series modelling. Most researchers have employed Autoregressive Integrated Moving Average and Logistic Regression techniques, and Adaptive Neuro-Fuzzy Inference System has rarely been used to analyse time-series data. Traditional time-series forecasting models assume a linear relationship between variables, while there are nonlinear and complex components in air pollution modelling. This study aimed to respond to this limitation by improving the accuracy of the daily prediction of pollutants via time-series data analysis by using Adaptive Neuro-Fuzzy Inference System modelling. A nonlinear multivariate regression model was developed and experimentally refined to obtain the least error possible. Data on pollutants containing CO, SO2, O-3, and NO2 are collected from a single monitoring point in Tehran. The process of the developing the model begins by breaking down the data sets into training, testing, and validation set at a random ratio of 80%, 10%, and 10%. For the prediction of CO, SO2, O-3, and NO2, the coefficients of determination are calculated as 0.8686, 0.8011, 0.8350 and 0.7640, and these values for the semi-experimental model were 0.8445, 0.8001, 0.7830 and 0.7602. According to the performance indicators of both models, Adaptive Neuro-Fuzzy Inference System is more accurate in predicting time-series data than regression models. Reliable forecasting of future air quality would help governments develop policies and regulations to protect humans and ecosystems and achieve sustainable development. (C) 2020 Elsevier Ltd. All rights reserved.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20701 - Environmental and geological engineering, geotechnics
Result continuities
Project
<a href="/en/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Sustainable Process Integration Laboratory (SPIL)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Cleaner Production
ISSN
0959-6526
e-ISSN
1879-1786
Volume of the periodical
neuveden
Issue of the periodical within the volume
261
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
121218-121218
UT code for WoS article
000533538800002
EID of the result in the Scopus database
2-s2.0-85082700905