Air pollution prediction using semi-experimental regression model and Adaptive Neuro-Fuzzy Inference System

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Air pollution prediction using semi-experimental regression model and Adaptive Neuro-Fuzzy Inference System

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Air pollution prediction using semi-experimental regression model and Adaptive Neuro-Fuzzy Inference System

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20701 - Environmental and geological engineering, geotechnics

Projekt

<a href="/cs/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Laboratoř integrace procesů pro trvalou udržitelnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Cleaner Production

ISSN

0959-6526

e-ISSN

1879-1786

Svazek periodika

neuveden

Číslo periodika v rámci svazku

261

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

121218-121218

Kód UT WoS článku

000533538800002

EID výsledku v databázi Scopus

2-s2.0-85082700905