Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique

Improving the explainability of CNN-LSTM-based flood prediction with integrating SHAP technique

Convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) are popular deep learning architectures currently used for rapid flood simulations. However, deep learning algorithms are difficult to explain, like a “black box” that lacks insight. In order to reveal the intrinsic mec...

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Bibliographic Details
Main Authors: Hao Huang, Zhaoli Wang, Yaoxing Liao, Weizhi Gao, Chengguang Lai, Xushu Wu, Zhaoyang Zeng
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Ecological Informatics
Subjects:
Flood prediction
Explainability
Coupled CNN-LSTM model
SHAP technique
Input features
Online Access:http://www.sciencedirect.com/science/article/pii/S1574954124004461
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