BiLSTM-Kalman framework for precipitation downscaling under multiple climate change scenarios

Abstract Traditional downscaling techniques often fail to accurately represent critical extremes necessary for effective adaptation planning. This paper introduces the first application of Bidirectional Long Short-Term Memory (BiLSTM) networks with an adaptive Kalman filter for multi-scenario, high-...

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Κύριοι συγγραφείς:	Melika Jahangiri, Mahdi Asghari, Mohammad Hossein Niksokhan, Mohammad Reza Nikoo
Μορφή:	Artigo
Γλώσσα:	Inglês
Έκδοση:	Nature Portfolio 2025-07-01
Σειρά:	Scientific Reports
Θέματα:	Precipitation downscaling BiLSTM neural networks Adaptive Kalman filtering Extreme event forecasting Climate change scenarios Deep learning
Διαθέσιμο Online:	https://doi.org/10.1038/s41598-025-08264-z
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