Document detail
ID
oai:arXiv.org:2409.11640
Topic
Computer Science - Machine Learnin...Author
Choi, Yohan Choi, Boaz Choi, JachinCategory
Computer Science
Year
2024
listing date
9/25/2024
Keywords
pm2 monitoring air dataAbstract
Air pollution, particularly particulate matter (PM2.5), poses significant risks to public health and the environment, necessitating accurate prediction and continuous monitoring for effective air quality management.
However, air quality monitoring (AQM) data often suffer from missing records due to various technical difficulties.
This study explores the application of Sparse Identification of Nonlinear Dynamics (SINDy) for imputing missing PM2.5 data by predicting, using training data from 2016, and comparing its performance with the established Soft Impute (SI) and K-Nearest Neighbors (KNN) methods.
Choi, Yohan,Choi, Boaz,Choi, Jachin, 2024, Enhancing PM2.5 Data Imputation and Prediction in Air Quality Monitoring Networks Using a KNN-SINDy Hybrid Model
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