Knowledge Distillation for Improving Deep Learning‐Based Hydrological Prediction and Fine‐Tuning

Abstract This paper introduces knowledge distillation (KD) as a method for improving deep learning (DL)‐based hydrological prediction. The performance of DL models, including long short‐term memory networks (LSTMs), is highly dependent on model structure and training data quality, posing challenges...

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Autori principali:	M. S. Jahangir, J. Quilty, S. Steinschneider, J. Adamowski
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Wiley 2026-06-01
Serie:	Journal of Geophysical Research: Machine Learning and Computation
Soggetti:	knowledge distillation fine‐tuning hydrological forecasting long short‐term memory deep learning
Accesso online:	https://doi.org/10.1029/2025JH001081
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