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

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...

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Bibliographic Details
Main Authors: Israel Edem Agbehadji, Ibidun Christiana Obagbuwa
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Artificial Intelligence
Subjects:
generative additive model
post-hoc explanation
local interpretable model-agnostic explanation
deep learning
causal inference
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2025.1620019/full
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https://www.frontiersin.org/articles/10.3389/frai.2025.1620019/full

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