Spatial-Temporal Forecasting of Air Pollution in Saudi Arabian Cities Based on a Deep Learning Framework Enabled by AI.

ABSTRACT: Air pollution is steadily increasing due to industrialization, economic activities, and transportation. High levels pose a significant threat to human health and well-being worldwide. Saudi Arabia is a growing country with air quality indices ranging from moderate to unhealthy. Although there are many monitoring stations distributed throughout the country, mathematical modeling of air pollution is still crucial for health and environmental decision-making. From this perspective, in this study, a data-driven approach based on pollutant records and a Deep Learning (DL) Long Short-Term Memory (LSTM) algorithm is carried out to perform temporal modeling of selected pollutants (PM10, PM2.5, CO and O3) based on time series combined with a spatial modeling focused on selected cities (Riyadh, Jedd

SUBMITTER: Zrieq R 

PROVIDER: S-EPMC12390308 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature