Air quality historical correlation model based on time series.

ABSTRACT: Air quality is closely linked to human health and social development, making accurate air quality prediction highly significant. The Air Quality Index (AQI) is inherently a time series. However, most previous studies have overlooked its temporal features and have not thoroughly explored the relationship between pollutant emissions and air quality. To address this issue, this study establishes a historical correlation model for air quality based on a time series model-the Gaussian Hidden Markov Model (GHMM)-using industrial exhaust emissions and historical air quality data. Firstly, a traversal method is used to select the optimal number of hidden states for the GHMM. To optimize the traditional GHMM and reduce error accumulation in the prediction process, the Multi-day Weighted Matching me

SUBMITTER: Liu Y 

PROVIDER: S-EPMC11445545 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature