利用 KNN-SINDy 混合模型加强空气质量监测网络中的 PM2.5 数据推算和预测

arXiv - CS - Machine Learning Pub Date : 2024-09-18 DOI:arxiv-2409.11640

Yohan Choi, Boaz Choi, Jachin Choi

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引用次数: 0

摘要

空气污染，尤其是颗粒物（PM2.5），对公众健康和环境构成了重大风险，需要准确预测和持续监测，以进行有效的空气质量管理。然而，由于各种技术上的困难，空气质量监测（AQM）数据往往存在记录缺失的问题。本研究利用 2016 年的训练数据，探索应用非线性动力学稀疏识别（SparseIdentification of Nonlinear Dynamics，SINDy）对缺失的 PM2.5 数据进行预测归因，并将其性能与已有的软归因（Soft Impute，SI）和 K-近邻（K-Nearest Neighbors，KNN）方法进行比较。

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Enhancing PM2.5 Data Imputation and Prediction in Air Quality Monitoring Networks Using a KNN-SINDy Hybrid Model

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.

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arXiv - CS - Machine Learning

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