Integration of Generative-Adversarial-Network-Based Data Compaction and Spatial Attention Transductive Long Short-Term Memory for Improved Rainfall–Runoff Modeling

Integration of Generative-Adversarial-Network-Based Data Compaction and Spatial Attention Transductive Long Short-Term Memory for Improved Rainfall–Runoff Modeling

This work presents a novel approach to rainfall–runoff modeling. We incorporate GAN-based data compaction into a spatial-attention-enhanced transductive long short-term memory (TLSTM) network. The GAN component reduces data dimensions while retaining essential features. This compaction enables the T...

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Bibliographic Details
Main Authors: Bahareh Ghanati, Joan Serra-Sagristà
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Remote Sensing
Subjects:
rainfall–runoff modeling
water resource management
climate change
long short-term memory style
generative adversarial network
autoencoder
Online Access:https://www.mdpi.com/2072-4292/16/20/3889

Internet

https://www.mdpi.com/2072-4292/16/20/3889

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