Unsupervised machine learning in air pollution epidemiology in South Africa

Domain:

healthcareenvironment and energy

Record type:

software

Creator:

NanWasJan

Host:

This dataset consist of different scripts and do files, used to achieve objectives to assess the applicability of machine learning in air pollution epidemiology in South Africa. The STATA do files were used to investigate the artificial intelligence (AI) survey distributed among postgraduate diploma students at the School of Health Systems and Public Health. R scripts were used for data imputation i.e., kalman, mice and mtsdi imputation, for the missing air pollution data and meteorological conditions. R scripts were also used for classification and regression trees to investigate joint effects of PM10, PM2.5, NO2, SO2 and O3 on respiratory and cardiovascular hospital admissions. Again presented are the R scripts for the unsupervised machine learning clustering methods i.e., k-means clustering, spectral clustering, dbscan clustering for joint effects for PM10, PM2.5, NO2, SO2 and O3 on respiratory and cardiovascular hospital admissions.

Visit

figshare.com

Licenses

CC BY 4.0

Reducing Air Pollution through Machine Learning

This paper presents a data-driven approach to mitigate the effects of air pollution from industrial

Air pollution particulate matter (PM2.5) prediction in South African cities using machine learning techniques

Background Air pollution contributes to the most severe environmental and health problems due to in

Air pollution exposure assessment in sparsely monitored settings; applying machine-learning methods with remote sensing data in South Africa.

Predictive Machine Learning Modeling of Urban Traffic Air Pollution:

Air pollution in Africa is a growing yet often overlooked threat, worsened by rapid industrializatio

Air Pollution Dispersion over Durban, South Africa

Air pollution dispersion over Durban is studied using satellite, reanalysis and in situ measurements