Documentdetail
ID kaart
oai:arXiv.org:2409.11640
Onderwerp
Computer Science - Machine Learnin...Auteur
Choi, Yohan Choi, Boaz Choi, JachinCategorie
Computer Science
Jaar
2024
vermelding datum
25-09-2024
Trefwoorden
pm2 monitoring air dataBeschrijving
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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