Developing an explainable deep learning module based on the LSTM framework for flood prediction

Developing an explainable deep learning module based on the LSTM framework for flood prediction

Long short-term memory (LSTM) networks have become indispensable tools in hydrological modeling due to their ability to capture long-term dependencies, handle non-linear relationships, and integrate multiple data sources but suffer from limited interpretability due to their black box nature. To addr...

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Bibliographic Details
Main Authors: Zhi Zhang, Dagang Wang, Yiwen Mei, Jinxin Zhu, Xusha Xiao
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Water
Subjects:
LSTM
explainable AI
interpretability
gated module
catchment analysis
flood mechanisms
Online Access:https://www.frontiersin.org/articles/10.3389/frwa.2025.1562842/full
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