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Machine and deep learning for modelling heat-health relationships.


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Boudreault, Jérémie; Campagna, Céline et Chebana, Fateh ORCID logoORCID: https://orcid.org/0000-0002-3329-8179 (2023). Machine and deep learning for modelling heat-health relationships. Science of The Total Environment , vol. 892 . p. 164660. DOI: 10.1016/j.scitotenv.2023.164660.

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Extreme heat events pose a significant threat to population health that is amplified by climate change. Traditionally, statistical models have been used to model heat-health relationships, but they do not consider potential interactions between temperature-related and air pollution predictors. Artificial intelligence (AI) methods, which have gained popularity for health applications in recent years, can account for these complex and non-linear interactions, but have been underutilized in modelling heat-related health impacts. In this paper, six machine and deep learning models were considered to model the heat-mortality relationship in Montreal (Canada) and compared to three statistical models commonly used in the field. Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Single- and Multi-Layer Perceptrons (SLP and MLP), Long Short-Term Memory (LSTM), Generalized Linear and Additive Models (GLM and GAM), and Distributed Lag Non-Linear Model (DLNM) were employed. Heat exposure was characterized by air temperature, relative humidity and wind speed, while air pollution was also included in the models using five pollutants. The results confirmed that air temperature at lags of up to 3 days was the most important variable for the heat-mortality relationship in all models. NO₂ concentration and relative humidity (at lags 1 to 3 days) were also particularly important. Ensemble tree-based methods (GBM and RF) outperformed other approaches to model daily mortality during summer months based on three performance criteria. However, a partial validation during two recent major heatwaves highlighted that non-linear statistical models (GAM and DLNM) and simpler decision tree may more closely reproduce the spike of mortality observed during such events. Hence, both machine learning and statistical models are relevant for modelling heat-health relationships depending on the end user goal. Such extensive comparative analysis should be extended to other health outcomes and regions.

Type de document: Article
Mots-clés libres: mortality; temperature; humidity; air pollution; machine learning; deep learning
Centre: Centre Eau Terre Environnement
Date de dépôt: 02 nov. 2023 14:02
Dernière modification: 02 nov. 2023 14:02
URI: https://espace.inrs.ca/id/eprint/13709

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