Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants

Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants

The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the inter-correlation between dominant air pollution index (API) for PM10 percentile v...

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Bibliographic Details
Main Authors: Omar F. Althuwaynee, Abdul‐Lateef Balogun, Wesam Al Madhoun
Format: Article
Language:English
Published: Taylor & Francis Group 2020-02-01
Series:GIScience & Remote Sensing
Subjects:
air quality
machine learning
pm10
r programming
sustainability
Online Access:http://dx.doi.org/10.1080/15481603.2020.1712064

Internet

http://dx.doi.org/10.1080/15481603.2020.1712064

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