f28751a6a33b55372753555ee7b70144 RemSen_a2024v16n20p3889.pdf b9a438a2be47927c00f90930d0be0a49ed77b652 RemSen_a2024v16n20p3889.pdf 17887b6166a99dd306a6b7d14c87e7cd57e95c820ca9b45a474c17c7366618c3 RemSen_a2024v16n20p3889.pdf Title: Integration of Generative-Adversarial-Network-Based Data Compaction and Spatial Attention Transductive Long Short-Term Memory for Improved Rainfall–Runoff Modeling Subject: 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 TLSTM to capture complex temporal dependencies in rainfall–runoff patterns more effectively. When tested on the CAMELS dataset, the model significantly outperforms benchmark LSTM-based models. For 8-day runoff forecasts, our model achieves an NSE of 0.536, compared to 0.326 from the closest competitor. The integration of GAN-based feature extraction with spatial attention mechanisms improves predictive accuracy, particularly for peak-flow events. This method offers a powerful solution for addressing current challenges in water resource management and disaster planning under extreme climate conditions. Keywords: rainfall–runoff modeling; water resource management; climate change; long short-term memory style; generative adversarial network; autoencoder Author: Bahareh Ghanati and Joan Serra-Sagristà Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Tue Oct 22 15:55:03 2024 CEST ModDate: Tue Oct 22 16:22:41 2024 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 18 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 3829162 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- WNWTFR+VnURWPalladioL-Bold Type 1 Custom yes yes yes 10 0 HCZEBL+URWPalladioL-Bold Type 1 Custom yes yes yes 16 0 ZWCXDW+URWPalladioL-Roma Type 1 Custom yes yes yes 22 0 ZJXUJK+URWPalladioL-Ital Type 1 Custom yes yes yes 27 0 SSXPJJ+VnURWPalladioL Type 1 Custom yes yes yes 32 0 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PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Autoencoder Structure Destination: subsection.2.1 Item: Title: Training Methodology: Modified GAN Framework Destination: subsection.2.2 Item: Title: Results Destination: section.3 Item: Title: Discussion Destination: section.4 Item: Title: Conclusions Destination: section.5 Item: Title: References Destination: appendix.A. Info: Title: Integration of Generative-Adversarial-Network-Based Data Compaction and Spatial Attention Transductive Long Short-Term Memory for Improved Rainfall Runoff Modeling Author: Bahareh Ghanati and Joan Serra-Sagristà Subject: 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 TLSTM to capture complex temporal dependencies in rainfall runoff patterns more effectively. When tested on the CAMELS dataset, the model significantly outperforms benchmark LSTM-based models. For 8-day runoff forecasts, our model achieves an NSE of 0.536, compared to 0.326 from the closest competitor. The integration of GAN-based feature extraction with spatial attention mechanisms improves predictive accuracy, particularly for peak-flow events. This method offers a powerful solution for addressing current challenges in water resource management and disaster planning under extreme climate conditions. 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