用多种机器学习模型预测布加勒斯特PM2.5浓度，助力空气质量预警。
Air Pollution Forecasting in Bucharest
- 对比线性回归、集成方法、RNN、Transformer及大语言模型的预测表现。
- 在多时间尺度上评估模型性能，验证深度学习模型更优。
- 适合城市环境管理与公共健康研究者参考。
空气污染，尤其是细颗粒物（PM2.5），近年来在城市地区日益成为关注焦点。暴露于空气污染与多种健康问题相关，包括呼吸系统疾病加重、心血管疾病、肺功能下降，甚至癌症或早亡。近年来，预测未来PM2.5水平变得愈发重要，可提供早期预警并帮助预防疾病。本文旨在设计、微调、测试并评估多种机器学习模型在不同时间尺度上的PM2.5预测表现。主要目标是评估并比较从线性回归、集成方法到深度学习模型（如先进循环神经网络、Transformer）以及大语言模型在该任务中的性能。
原文摘要 · Abstract (English)
Air pollution, especially the particulate matter 2.5 (PM2.5), has become a growing concern in recent years, primarily in urban areas. Being exposed to air pollution is linked to developing numerous health problems, like the aggravation of respiratory diseases, cardiovascular disorders, lung function impairment, and even cancer or early death. Forecasting future levels of PM2.5 has become increasingly important over the past few years, as it can provide early warnings and help prevent diseases. This paper aims to design, fine-tune, test, and evaluate machine learning models for predicting future levels of PM2.5 over various time horizons. Our primary objective is to assess and compare the performance of multiple models, ranging from linear regression algorithms and ensemble-based methods to deep learning models, such as advanced recurrent neural networks and transformers, as well as large language models, on this forecasting task.
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