用新模型填补空气质量数据空缺，提升污染预测精度。

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

• 结合KNN与非线性动力学建模，自动学习污染变化规律
• 在真实监测站数据上，误差比传统方法降低18%-27%
• 适合环保部门和城市空气质量研究者使用

细颗粒物（PM2.5）空气污染对公共健康和环境构成重大威胁，需要精准预测与持续监测以实现有效管理。然而，由于各种技术问题，空气质量监测（AQM）数据常存在缺失记录。本研究探索利用稀疏非线性动力学识别（SINDy）方法，基于2016年训练数据预测并填补缺失的PM2.5数据，并与成熟的软填充（Soft Impute, SI）和K近邻（KNN）方法进行性能比较。实验结果表明，所提出的KNN-SINDy混合模型在多个真实监测站点的数据集上显著优于基线方法，平均均方根误差（RMSE）降低18%至27%，尤其在长期缺失和非线性波动场景下表现更优。该方法能够捕捉污染物时间序列中的内在动态模式，为高可靠性空气质量数据重建提供了新思路。

原文摘要 · Abstract (English)

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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