1. Spatial air quality prediction in urban areas via message passing.
- Author
-
Calo, Sergio, Bistaffa, Filippo, Jonsson, Anders, Gómez, Vicenç, and Viana, Mar
- Subjects
- *
AIR quality , *CITIES & towns , *MARKOV random fields , *GRAPH neural networks , *AIR pollution , *TANNER graphs - Abstract
Air pollution in urban areas poses a significant and pressing challenge for modern society. Unfortunately, the existing network of pollution detectors in many cities is limited in scope and fails to adequately cover the entire geographical area. Consequently, the implementation of spatial prediction algorithms becomes essential to generate high-resolution data. In this paper, we introduce two significant contributions: 1) We formalize the air pollution prediction problem as a Maximum A Posteriori (MAP) estimate within the framework of a Markov Random Field and 2) we propose a message-passing algorithm, which stands out as an efficient solution that surpasses the current state of the art. The experimental procedure has been carried out using the case study of the city of Barcelona, based on a dataset extracted from the BCN Open Data portal. [Display omitted] • Spatial prediction of air quality is tackled with a novel formalization and method. • The use of a Message Passing style algorithm is proposed. • The model is trained with data specific to the city of Barcelona. • The presented method outperforms other existing methods for the given problem. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF