Air Quality Prediction and Control Systems Using Machine Learning and Adaptive Neuro-Fuzzy Inference System

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F24%3A00012587" target="_blank" >RIV/46747885:24220/24:00012587 - isvavai.cz</a>

Alternative codes found

RIV/46747885:24620/24:00012587

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S2405844024158148" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2405844024158148</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.heliyon.2024.e39783" target="_blank" >10.1016/j.heliyon.2024.e39783</a>

Result language

angličtina

Original language name

Air Quality Prediction and Control Systems Using Machine Learning and Adaptive Neuro-Fuzzy Inference System

Original language description

Accurately predicting air quality concentrations is a challenging task due to the complex interactions of pollutants and their reliance on nonlinear processes. This study introduces an innovative approach in environmental engineering, employing artificial intelligence techniques to forecast air quality in Semnan, Iran. Comprehensive data on seven different pollutants was initially collected and analyzed. Then, several machine learning (ML) models were rigorously evaluated for their performance, and a detailed analysis was conducted. By incorporating these advanced technologies, the study aims to create a reliable framework for air quality prediction, with a particular focus on the case study in Iran. The results indicated that the adaptive neuro-fuzzy inference system (ANFIS) was the most effective method for predicting air quality across different seasons, showing high reliability across all datasets.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

21100 - Other engineering and technologies

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Name of the periodical

Heliyon

ISSN

2405-8440

e-ISSN

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Volume of the periodical

10

Issue of the periodical within the volume

21

Country of publishing house

GB - UNITED KINGDOM

Number of pages

17

Pages from-to

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UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85208190903