An Optimized Hybrid Forecasting Model and Its Application to Air Pollution Concentration

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F20%3A39916666" target="_blank" >RIV/00216275:25410/20:39916666 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s13369-020-04572-w" target="_blank" >https://link.springer.com/article/10.1007/s13369-020-04572-w</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s13369-020-04572-w" target="_blank" >10.1007/s13369-020-04572-w</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Optimized Hybrid Forecasting Model and Its Application to Air Pollution Concentration

Popis výsledku v původním jazyce

Previous literature suggested that intuitionistic fuzzy inference systems (IFISs) can offer a good forecasting model and intimately linked to the notion of uncertain parameters. However, their performance can be severely degraded by the presence of missing data and less regulated local optima. This study proposes a hybrid IFIS model by assimilating the probabilistic principal component analysis (PPCA) to enhance preprocessing data and particle swarm optimization (PSO) algorithm to optimize the performance of the forecasting model. The main purpose of the PPCA is to diminish outliers affected by defective values and missing values within experimental data. The PSO optimization algorithm is used to tune the parameters of IFIS and thus elevate the prediction performance of the IFIS. Extensive experimental data on meteorological parameters that are recognized as driving factors of tropospheric pollution were employed to study the benefits of the proposed hybrid model. Comparable three error measures are presented to check the performance of the proposed model against the other models. The error analysis result clearly highlights that the proposed hybrid model is performed better compared to the other IFIS-based models and the well-known existing models.

Název v anglickém jazyce

An Optimized Hybrid Forecasting Model and Its Application to Air Pollution Concentration

Popis výsledku anglicky

Previous literature suggested that intuitionistic fuzzy inference systems (IFISs) can offer a good forecasting model and intimately linked to the notion of uncertain parameters. However, their performance can be severely degraded by the presence of missing data and less regulated local optima. This study proposes a hybrid IFIS model by assimilating the probabilistic principal component analysis (PPCA) to enhance preprocessing data and particle swarm optimization (PSO) algorithm to optimize the performance of the forecasting model. The main purpose of the PPCA is to diminish outliers affected by defective values and missing values within experimental data. The PSO optimization algorithm is used to tune the parameters of IFIS and thus elevate the prediction performance of the IFIS. Extensive experimental data on meteorological parameters that are recognized as driving factors of tropospheric pollution were employed to study the benefits of the proposed hybrid model. Comparable three error measures are presented to check the performance of the proposed model against the other models. The error analysis result clearly highlights that the proposed hybrid model is performed better compared to the other IFIS-based models and the well-known existing models.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Arabian Journal for Science and Engineering

ISSN

2193-567X

e-ISSN

—

Svazek periodika

45

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

23

Strana od-do

9953-9975

Kód UT WoS článku

000531765700003

EID výsledku v databázi Scopus

2-s2.0-85084470151