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A Simple Baseline for Adversarial Domain Adaptation-based Unsupervised Flood Forecasting
[article]
2022
arXiv
pre-print
Flood disasters cause enormous social and economic losses. However, both traditional physical models and learning-based flood forecasting models require massive historical flood data to train the model parameters. When come to some new site that does not have sufficient historical data, the model performance will drop dramatically due to overfitting. This technical report presents a Flood Domain Adaptation Network (FloodDAN), a baseline of applying Unsupervised Domain Adaptation (UDA) to the
arXiv:2206.08105v1
fatcat:r3hetbe4kbdm7c3j2tfw4atyzi