Application of Random Forest Method for Analysis of Gas Sensor Readouts From Mold-threatened Buildings

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F23%3A00370799" target="_blank" >RIV/68407700:21110/23:00370799 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1063/5.0147200" target="_blank" >http://dx.doi.org/10.1063/5.0147200</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/5.0147200" target="_blank" >10.1063/5.0147200</a>

Result language

angličtina

Original language name

Application of Random Forest Method for Analysis of Gas Sensor Readouts From Mold-threatened Buildings

Original language description

Humidity increase in buildings frequently leads to the growth of mold, which is one of significant factors for evaluation of Sick Building Syndrome. The assessment of mold contamination level in buildings based on gas sensors array readouts is considered as a cheap and fast detection technique; nonetheless, interpretation of signals is quite complex, mostly because the signals obtained from sensors are multidimensional. Furthermore, there is no sole reference method used in practice. The signals analyzed in the original multi-dimensional space are characterized by high variability, depending on the conditions in the tested buildings. In such a situation, the random forest methodology can be applied, which till now has been successfully involved to a wide range of prediction problems and has few parameters to tune. Aside from being simple to use, the method is generally recognized for its accuracy and ability to deal with small sample sizes and high-dimensional feature spaces. At the same time, it is easily parallelizable and therefore has the potential to deal with large real-life systems. This supervised learning procedure operates according to the simple but effective “divide and conquer” principle: sample fractions of the data, grow a randomized tree predictor on each small piece, then paste (aggregate) these predictors together.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20506 - Coating and films

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

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

Article name in the collection

Central European Symposium on Thermophysics 2022 (CEST 2022)

ISBN

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ISSN

1551-7616

e-ISSN

1551-7616

Number of pages

4

Pages from-to

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Publisher name

AIP Conference Proceedings

Place of publication

New York

Event location

Olomouc

Event date

Aug 31, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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