Machine Learning Approach To Estimate Hourly Exposure to Fine Particulate Matter for Urban, Rural, and Remote Populations during Wildfire Seasons

2018 ◽  
Vol 52 (22) ◽  
pp. 13239-13249 ◽  
Author(s):  
Jiayun Yao ◽  
Michael Brauer ◽  
Sean Raffuse ◽  
Sarah B. Henderson
Keyword(s):  
Machine Learning ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Learning Approach ◽  
Rural And Remote ◽  
Fine Particulate ◽  
Machine Learning Approach ◽  
Urban Rural

Evaluate and Predict Concentration of Particulate Matter (PM10) Using Machine Learning Approach

2019 ◽  
Vol 1 (1) ◽  
pp. 11-23
Author(s):  
M. F. Mridha ◽  
Md. Abdul Hamid ◽  
Shaon Hossain Sani ◽  
Akramkhan Rony ◽  
Seungmin Oh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Particulate Matter ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Urban–Rural Gradients Predict Educational Gaps: Evidence from a Machine Learning Approach Involving Academic Performance and Impervious Surfaces in Ecuador

2021 ◽  
Vol 10 (12) ◽  
pp. 830
Author(s):  
Fabián Santos-García ◽  
Karina Delgado Valdivieso ◽  
Andreas Rienow ◽  
Joaquín Gairín
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Cognitive Skills ◽  
Learning Approach ◽  
Impervious Surfaces ◽  
School Students ◽  
Urban Transition ◽  
Natural Region ◽  
Machine Learning Approach ◽  
Urban Rural

Academic performance (AP) is explained by a multitude of factors, principally by those related to socioeconomic, cultural, and educational environments. However, AP is less understood from a spatial perspective. The aim of this study was to investigate a methodology using a machine learning approach to determine which answers from a questionnaire-based survey were relevant for explaining the high AP of secondary school students across urban–rural gradients in Ecuador. We used high school locations to construct individual datasets and stratify them according to the AP scores. Using the Boruta algorithm and backward elimination, we identified the best predictors, classified them using random forest, and mapped the AP classification probabilities. We summarized these results as frequent answers observed for each natural region in Ecuador and used their probability outputs to formulate hypotheses with respect to the urban–rural gradient derived from annual maps of impervious surfaces. Our approach resulted in a cartographic analysis of AP probabilities with overall accuracies around 0.83–0.84% and Kappa values of 0.65–0.67%. High AP was primarily related to answers regarding the academic environment and cognitive skills. These identified answers varied depending on the region, which allowed for different interpretations of the driving factors of AP in Ecuador. A rural-to-urban transition ranging 8–17 years was found to be the timespan correlated with achievement of high AP.

Spatiotemporal Prediction of Fine Particulate Matter During the 2008 Northern California Wildfires Using Machine Learning

2015 ◽  
Vol 49 (6) ◽  
pp. 3887-3896 ◽  
Author(s):  
Colleen E. Reid ◽  
Michael Jerrett ◽  
Maya L. Petersen ◽  
Gabriele G. Pfister ◽  
Philip E. Morefield ◽  
...  
Keyword(s):  
Machine Learning ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Northern California ◽  
Fine Particulate
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