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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/46747885:24620/24:00012587

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

21100 - Other engineering and technologies

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Heliyon

ISSN

2405-8440

e-ISSN

—

Svazek periodika

10

Číslo periodika v rámci svazku

21

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

17

Strana od-do

—

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85208190903