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Machine Learning Approach-based Big Data Imputation Methods for Outdoor Air Quality forecasting

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Title Machine Learning Approach-based Big Data Imputation Methods for Outdoor Air Quality forecasting
 
Creator D, Narasimhan
M, Vanitha
 
Subject Air quality
Big data analytics
Classification
Ensemble
Multiple imputation
 
Description 338-347
Missing data from ambient air databases is a typical issue, but it is much worse in small towns or cities. Missing data is a
significant concern for environmental epidemiology. These settings have high pollution exposure levels worldwide, and
dataset gaps obstruct health investigations that could later affect local and international policies. When a substantial number
of observations contain missing values, the standard errors increase due to the smaller sample size, which may significantly
affect the final result. Generally, the performance of various missing value imputation algorithms is proportional to the size
of the database and the percentage of missing values within it. This paper proposes and demonstrates an ensemble –
imputation – classification framework approach to rebuild air quality information using a dataset from Beijing, China, to
forecast air quality. Various single and multiple imputation procedures are utilized to fill the missing records. Then
ensemble of diverse classifiers is used on the imputed data to find the air pollution level. The recommended model aims to
reduce the error rate and improve accuracy. Extensive testing of datasets with actual missing values has revealed that the
suggested methodology significantly enhances the air quality forecasting model’s accuracy with multiple imputation and
ensemble techniques when compared to other conventional single imputation techniques.
 
Date 2023-03-07T12:01:13Z
2023-03-07T12:01:13Z
2023-03
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61516
https://doi.org/10.56042/jsir.v82i03.71764
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.82(03) [March 2023]