Explainable sequence-to-sequence GRU neural network for pollution forecasting.

ABSTRACT: The goal of pollution forecasting models is to allow the prediction and control of the air quality. Non-linear data-driven approaches based on deep neural networks have been increasingly used in such contexts showing significant improvements w.r.t. more conventional approaches like regression models and mechanistic approaches. While such deep learning models were deemed for a long time as black boxes, recent advances in eXplainable AI (XAI) allow to look through the model's decision-making process, providing insights into decisive input features responsible for the model's prediction. One XAI technique to explain the predictions of neural networks which was proven useful in various domains is Layer-wise Relevance Propagation (LRP). In this work, we extend the LRP technique to a sequence-to

SUBMITTER: Mirzavand Borujeni S 

PROVIDER: S-EPMC10279754 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature