Enhancing PM2.5 Data Imputation and Prediction in Air Quality Monitoring Networks Using a KNN-SINDy Hybrid Model
It addresses data gaps in air quality monitoring for public health and environmental management, but appears incremental as it combines existing methods.
This study tackled the problem of missing PM2.5 data in air quality monitoring by applying a KNN-SINDy hybrid model for imputation, achieving competitive performance compared to established methods like Soft Impute and KNN.
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.