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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
An Optimized Hybrid Forecasting Model and Its Application to Air Pollution Concentration
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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