Extracting mass concentration time series features for classifcation of indoor and outdoor atmospheric particulates

Hussain, Lal; Aziz, Wajid; Saeed, Sharjil; Rafque, Muhammad; Nadeem, Malik Sajjad Ahmed; Shim, Seong-O; Aftar, Sania; Pirzada, Jawad-ur-Rehman

doi:10.1007/s11600-020-00443-y

Artykuł - szczegóły

Tytuł artykułu

Extracting mass concentration time series features for classifcation of indoor and outdoor atmospheric particulates

Autorzy

Hussain Lal , Aziz Wajid , Saeed Sharjil , Rafque Muhammad , Nadeem Malik Sajjad Ahmed , Shim Seong-O , Aftar Sania , Pirzada Jawad-ur-Rehman

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-020-00443-y

Warianty tytułu

Języki publikacji

Abstrakty

Particulate matters (PMs) are considered as one of the air pollutants generally associated with poor air quality in both outdoor and indoor environments. The composition, distribution and size of these particles hazardously afect the human health causing cardiovascular health problems, lung dysfunction, respiratory problems, chronic obstructive pulmonary disease and lungs cancer. Classifcation models developed by analyzing mass concentration time series data of atmospheric particulate matter can be used for the prediction of air quality and for issuing warnings to protect the health of the public. In this study, mass concentration time series data of both outdoor and indoor particulates matters PM_2.5 (aerodynamics size up to 2.5 μ) and PM_10.0 (aerodynamics size up to 10.0 μ) were acquired using Haz-Dust EPAM-5000 from six diferent locations of the Muzafarabad city, Azad Kashmir. The linear and nonlinear approaches were used to extract mass concentration time series features of the indoor and outdoor atmospheric particulates. These features were given as an input to the robust machine learning classifers. The support vector machine (SVM) kernels, ensemble classifers, decision tree and K-nearest neighbors (KNN) are used to classify the indoor and outdoor particulate matter time series. The performance was estimated in terms of area under the curve (AUC), accuracy, true negative rate, true positive rate, negative predictive value and positive predictive value. The highest accuracy (95.8%) was obtained using cubic and coarse Gaussian SVM along with the cosine and cubic KNN, while the highest AUC, i.e., 1.00, is obtained using fne Gaussian and cubic SVM as well as with the cubic and weighted KNN.

Słowa kluczowe

classification decision tree ensemble classifier support vector machine k-Nearest neighbors k-NN particular matters

klasyfikacja drzewo decyzyjne klasyfikator zespołu maszyna wektorów nośnych

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2020

Tom

Vol. 68, no. 3

Strony

945--963

Opis fizyczny

Bibliogr. 99 poz.

Twórcy

autor

Hussain Lal

lal.hussain@ajku.edu.pk

Department of Computer Sciences and Information Technology, University of Azad Jammu & Kashmir, City Campus, Muzafarabad, AJ&K 13100, Pakistan

autor

Aziz Wajid

Department of Computer Sciences and Information Technology, University of Azad Jammu & Kashmir, City Campus, Muzafarabad, AJ&K 13100, Pakistan
College of Computer Science and Engineering, University of Jeddah, Jeddah, Kingdom of Saudi Arabia

autor

Saeed Sharjil

Department of Computer Sciences and Information Technology, University of Azad Jammu & Kashmir, City Campus, Muzafarabad, AJ&K 13100, Pakistan

autor

Rafque Muhammad

Department of Physics, University of Azad Jammu & Kashmir, Chehla Campus, Muzafarabad, AJ&K 13100, Pakistan

autor

Nadeem Malik Sajjad Ahmed

Department of Computer Sciences and Information Technology, University of Azad Jammu & Kashmir, City Campus, Muzafarabad, AJ&K 13100, Pakistan

autor

Shim Seong-O

College of Computer Science and Engineering, University of Jeddah, Jeddah, Kingdom of Saudi Arabia

autor

Aftar Sania

Department of Physics, University of Azad Jammu & Kashmir, Chehla Campus, Muzafarabad, AJ&K 13100, Pakistan

autor

Pirzada Jawad-ur-Rehman

Department of Computer Sciences and Information Technology, University of Azad Jammu & Kashmir, City Campus, Muzafarabad, AJ&K 13100, Pakistan

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)

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Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-beb5da20-e5a5-415e-a6de-7f4f31ccf130