Model-based imputation of sound level data at thoroughfare using computational intelligence

The aim of the paper was to present the methodology of imputation of the missing sound level data, for a period of several months, in many noise monitoring stations located at thoroughfares by applying one model which describes variability of sound level within the tested period. To build the model,...

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Autor principal:	Kekez Michał
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	imputation monitoring station sound level random forest scaling functions Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/f54bdd5c1d3b4502bfad798e36b828b3
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spelling	oai:doaj.org-article:f54bdd5c1d3b4502bfad798e36b828b32021-12-05T14:10:46ZModel-based imputation of sound level data at thoroughfare using computational intelligence2391-543910.1515/eng-2021-0051https://doaj.org/article/f54bdd5c1d3b4502bfad798e36b828b32021-03-01T00:00:00Zhttps://doi.org/10.1515/eng-2021-0051https://doaj.org/toc/2391-5439The aim of the paper was to present the methodology of imputation of the missing sound level data, for a period of several months, in many noise monitoring stations located at thoroughfares by applying one model which describes variability of sound level within the tested period. To build the model, at first the proper set of input attributes was elaborated, and training dataset was prepared using recorded equivalent sound levels at one of thoroughfares. Sound level values in the training data were calculated separately for the following 24-hour sub-intervals: day (6–18), evening (18–22) and night (22–6). Next, a computational intelligence approach, called Random Forest was applied to build the model with the aid of Weka software. Later, the scaling functions were elaborated, and the obtained Random Forest model was used to impute data at two other locations in the same city, using these scaling functions. The statistical analysis of the sound levels at the abovementioned locations during the whole year, before and after imputation, was carried out.Kekez MichałDe Gruyterarticleimputationmonitoring stationsound levelrandom forestscaling functionsEngineering (General). Civil engineering (General)TA1-2040ENOpen Engineering, Vol 11, Iss 1, Pp 519-527 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	imputation monitoring station sound level random forest scaling functions Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	imputation monitoring station sound level random forest scaling functions Engineering (General). Civil engineering (General) TA1-2040 Kekez Michał Model-based imputation of sound level data at thoroughfare using computational intelligence
description	The aim of the paper was to present the methodology of imputation of the missing sound level data, for a period of several months, in many noise monitoring stations located at thoroughfares by applying one model which describes variability of sound level within the tested period. To build the model, at first the proper set of input attributes was elaborated, and training dataset was prepared using recorded equivalent sound levels at one of thoroughfares. Sound level values in the training data were calculated separately for the following 24-hour sub-intervals: day (6–18), evening (18–22) and night (22–6). Next, a computational intelligence approach, called Random Forest was applied to build the model with the aid of Weka software. Later, the scaling functions were elaborated, and the obtained Random Forest model was used to impute data at two other locations in the same city, using these scaling functions. The statistical analysis of the sound levels at the abovementioned locations during the whole year, before and after imputation, was carried out.
format	article
author	Kekez Michał
author_facet	Kekez Michał
author_sort	Kekez Michał
title	Model-based imputation of sound level data at thoroughfare using computational intelligence
title_short	Model-based imputation of sound level data at thoroughfare using computational intelligence
title_full	Model-based imputation of sound level data at thoroughfare using computational intelligence
title_fullStr	Model-based imputation of sound level data at thoroughfare using computational intelligence
title_full_unstemmed	Model-based imputation of sound level data at thoroughfare using computational intelligence
title_sort	model-based imputation of sound level data at thoroughfare using computational intelligence
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/f54bdd5c1d3b4502bfad798e36b828b3
work_keys_str_mv	AT kekezmichał modelbasedimputationofsoundleveldataatthoroughfareusingcomputationalintelligence
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Model-based imputation of sound level data at thoroughfare using computational intelligence

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