Forecasting Air Pollution by Adaptive Neuro Fuzzy Inference System

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F19%3APU135405" target="_blank" >RIV/00216305:26210/19:PU135405 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.23919/SpliTech.2019.8783075" target="_blank" >http://dx.doi.org/10.23919/SpliTech.2019.8783075</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/SpliTech.2019.8783075" target="_blank" >10.23919/SpliTech.2019.8783075</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Forecasting Air Pollution by Adaptive Neuro Fuzzy Inference System

Popis výsledku v původním jazyce

Air pollution causes a variety of adverse effects on humans such as illness or even death and damages the living organisms and the natural environment. This environmental issue needs to be controlled using various application and technology to estimate the composition of multiple pollutants in the atmosphere for a specified time and location. The present study aims to develop a system for air pollution forecasting using an adaptive neuro-fuzzy inference system. This method is a type of artificial neural network that integrates both neural networks and fuzzy logic principles. The adaptive neuro-fuzzy inference system calculations include four phases including implement fuzzy system, enter parameters, start the learning process, and verify the processed data. As a sample, the concentrations of atmospheric pollutant data recorded by sensors. The adaptive neuro-fuzzy inference system method predicts four air pollution indicator levels, including carbon monoxide, sulfur dioxide, nitrogen oxides, and trioxygen. The analysis results reveal that the mean absolute error of the adaptive neuro-fuzzy inference system method results is less than 15 %.

Název v anglickém jazyce

Forecasting Air Pollution by Adaptive Neuro Fuzzy Inference System

Popis výsledku anglicky

Air pollution causes a variety of adverse effects on humans such as illness or even death and damages the living organisms and the natural environment. This environmental issue needs to be controlled using various application and technology to estimate the composition of multiple pollutants in the atmosphere for a specified time and location. The present study aims to develop a system for air pollution forecasting using an adaptive neuro-fuzzy inference system. This method is a type of artificial neural network that integrates both neural networks and fuzzy logic principles. The adaptive neuro-fuzzy inference system calculations include four phases including implement fuzzy system, enter parameters, start the learning process, and verify the processed data. As a sample, the concentrations of atmospheric pollutant data recorded by sensors. The adaptive neuro-fuzzy inference system method predicts four air pollution indicator levels, including carbon monoxide, sulfur dioxide, nitrogen oxides, and trioxygen. The analysis results reveal that the mean absolute error of the adaptive neuro-fuzzy inference system method results is less than 15 %.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20402 - Chemical process engineering

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

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

2019 4TH INTERNATIONAL CONFERENCE ON SMART AND SUSTAINABLE TECHNOLOGIES (SPLITECH)

ISBN

978-953-290-091-0

ISSN

—

e-ISSN

—

Počet stran výsledku

3

Strana od-do

456-458

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Neuveden

Místo konání akce

Univ Split, Fac Elect Engn, Mech Engn & Naval Ar

Datum konání akce

18. 6. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000502810800083