Titre :

PmForecast: Leveraging Temporal LSTM to Deliver In situ Air Quality Predictions

Auteur(s) :

Rahmani, Maryam [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Crumeyrolle, Suzanne [Auteur]
Laboratoire d’Optique Atmosphérique - UMR 8518 [LOA]
Allegri-Martiny, Nadège [Auteur]
Centre de Recherches de Climatologie [UMR Biogéosciences] [CRC]
Taherkordi, Amir [Auteur]
Institutt for informatikk [Oslo] [IFI]
Rouvoy, Romain [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]

Titre de la revue :

Environmental Science and Pollution Research

Pagination :

51760-51773

Éditeur :

Springer Verlag

Date de publication :

2024-08-10

ISSN :

0944-1344

Mot(s)-clé(s) en anglais :

Air Quality Forecasting Air pollution PM2.5 Prediction Urban Air Quality LSTM
Air Quality Forecasting
Air pollution
PM2.5 Prediction
Urban Air Quality
LSTM

Discipline(s) HAL :

Informatique [cs]

Résumé en anglais : [en]

The physical and chemical properties of atmospheric aerosol particles are crucial in influencing global climate and ecosystem processes. Given the numerous studies highlighting adverse health effects from exposure to aerosol ...
Lire la suite >The physical and chemical properties of atmospheric aerosol particles are crucial in influencing global climate and ecosystem processes. Given the numerous studies highlighting adverse health effects from exposure to aerosol particulates, particularly PM, effective air quality management strategies are under consideration (Annesi-Maesano et al, 2007). Herein, we introduce a predictive model-PmForecast-employing a self-adaptive LSTM architecture to predict PM2.5 values in the real atmosphere. Specifically, we explore adopting a T-LSTM model to better benefit from temporal dimensions. PmForecast is strategically designed with four key phases: preprocessing, temporal attention, prediction horizon, and LSTM layers. By leveraging LSTM's significant predictive ability in time-series data, the inclusion of temporal attention enhances the model's specificity. Temporal dynamics modeling entails generating insights over time, utilizing temporal attention to extract essential characteristics from historical air pollutant concentrations, with the flexibility to adjust the historical data according to the forecasting period. To assess PmForecast, we consider measurements collected from the QameleO network, a sparse network of air-quality micro-stations deployed in Dijon, France. The self-adaptive capabilities of PmForecast allow the model to be dynamically updated, evaluating its performance and continuously tuning hyper-parameters based on the latest data trends. Our empirical evaluation reports that PmForecast outperforms the state of the art, achieving notable accuracy in both short-term and long-term predictions.The PmForecast deployment at scale can serve as a valuable tool for proactive decision-making and targeted interventions to mitigate the health risks associated with air pollution.</p>Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet Européen :

Programme for EArly-stage Researchers in Lille

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL