Abstract
Policymakers frequently analyze air quality and climate change in isolation, disregarding their interactions. This study explores the influence of specific climate factors on air quality by contrasting a regression model with K-Means Clustering, Hierarchical Clustering, and Random Forest techniques. We employ Physics-based Deep Learning (PBDL) and Long Short-Term Memory (LSTM) to examine the air pollution predictions. Our analysis utilizes ten years (2009-2018) of daily traffic, weather, and air pollution data from three major cities in Norway. Findings from feature selection reveal a correlation between rising heating degree days and heightened air pollution levels, suggesting increased heating activities in Norway are a contributing factor to worsening air quality. PBDL demonstrates superior accuracy in air pollution predictions compared to LSTM. This paper contributes to the growing literature on PBDL methods for more accurate air pollution predictions using environmental variables, aiding policymakers in formulating effective data-driven climate policies.
Abstract (translated)
政策制定者经常孤立地分析空气质量和气候变化，忽视其相互影响。本研究通过将回归模型与K-聚类、层次聚类和随机森林技术相比较，探讨了特定气候因素对空气质量的影响。我们使用基于物理的深度学习（PBDL）和长短时记忆（LSTM）来研究空气污染预测。我们的分析利用了挪威三个主要城市（2009-2018）的每日交通、天气和空气污染数据十年的时间。特征选择的结果表明，每日加热 degree days 上升与空气污染水平加剧之间存在关联，表明挪威挪威的加热活动是加剧空气污染的一个因素。与LSTM相比，PBDL在空气污染预测方面的准确性具有优势。本文为使用环境变量进行更准确空气污染预测的PBDL方法的文献贡献，为政策制定者制定有效的数据驱动气候政策提供了帮助。
URL
https://arxiv.org/abs/2405.04716
