A hybrid long short-term memory with generalized additive model and post-hoc explainable artificial intelligence with causal inference for air pollutants prediction in Kimberley, South Africa

The study addresses the problem of nonlinear characteristics of common air pollutants by proposing a deep learning time-series model based on the long short-term memory (LSTM) integrated with a generalized additive model (GAM). LSTM model captures both nonlinear relationships and temporal long-term...

ver descrição completa

Detalhes bibliográficos

Principais autores:	Israel Edem Agbehadji, Ibidun Christiana Obagbuwa
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Frontiers Media S.A. 2025-08-01
coleção:	Frontiers in Artificial Intelligence
Assuntos:	generative additive model post-hoc explanation local interpretable model-agnostic explanation deep learning causal inference
Acesso em linha:	https://www.frontiersin.org/articles/10.3389/frai.2025.1620019/full
Tags:	Adicionar Tag Sem tags, seja o primeiro a adicionar uma tag!