Comparison of Methods for Smoothing Environmental Data with an Application to Particulate Matter PM10

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F18%3A43913652" target="_blank" >RIV/62156489:43110/18:43913652 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.11118/actaun201866020453" target="_blank" >https://doi.org/10.11118/actaun201866020453</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.11118/actaun201866020453" target="_blank" >10.11118/actaun201866020453</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Methods for Smoothing Environmental Data with an Application to Particulate Matter PM10

Popis výsledku v původním jazyce

Data smoothing is often required within the environmental data analysis. A number of methods and algorithms that can be applied for data smoothing have been proposed. This paper gives an overview and compares the performance of different smoothing procedures that estimate the trend in the data, based on the surrounding noisy observations that can be applied on environmental data. The considered methods include kernel regression with both global and local bandwidth, moving average, exponential smoothing, robust repeated median regression, trend filtering and approach based on discrete Fourier and discrete wavelet transform. The methods are applied to real data obtained by measurement of PM10 concentrations and compared in a simulation study.

Název v anglickém jazyce

Comparison of Methods for Smoothing Environmental Data with an Application to Particulate Matter PM10

Popis výsledku anglicky

Data smoothing is often required within the environmental data analysis. A number of methods and algorithms that can be applied for data smoothing have been proposed. This paper gives an overview and compares the performance of different smoothing procedures that estimate the trend in the data, based on the surrounding noisy observations that can be applied on environmental data. The considered methods include kernel regression with both global and local bandwidth, moving average, exponential smoothing, robust repeated median regression, trend filtering and approach based on discrete Fourier and discrete wavelet transform. The methods are applied to real data obtained by measurement of PM10 concentrations and compared in a simulation study.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10700 - Other natural sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

ISSN

1211-8516

e-ISSN

—

Svazek periodika

66

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

11

Strana od-do

453-463

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85047624015