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 flood forecasting problem. Specifically, training of FloodDAN includes two stages: in the first stage, we train a rainfall encoder and a prediction head to learn general transferable hydrological knowledge on large-scale source domain data; in the second stage, we transfer the knowledge in the pretrained encoder into the rainfall encoder of target domain through adversarial domain alignment. During inference, we utilize the target domain rainfall encoder trained in the second stage and the prediction head trained in the first stage to get flood forecasting predictions. Experimental results on Tunxi and Changhua flood dataset show that FloodDAN can perform flood forecasting effectively with zero target domain supervision. The performance of the FloodDAN is on par with supervised models that uses 450-500 hours of supervision.

翻译：洪水灾害造成了巨大的社会和经济损失。然而,传统物理模型和基于学习的洪水预报模型都需要大量历史洪涝数据来培训模型参数。当来到一些没有足够历史数据的新地点时,模型性能会因过度配制而急剧下降。本技术报告提出了一个洪涝域适应网络(Floudddan),这是对洪水预报问题应用无人监督的海域适应(UDA)的基准。具体地说,洪水监测包括两个阶段:第一阶段,我们培训降雨编码器和预测头,以学习大规模源域数据的一般可转移水文知识;第二阶段,我们通过对抗性域校准,将培训前的编码器中的知识传输到目标域的降雨编码器中。在推断期间,我们利用了第二阶段培训的目标域降雨编码器和第一阶段培训的预测头,以获得洪水预报。Tunxi和Changhua洪水数据集的实验结果显示,FldDAN能够以零目标域监测方式有效地进行洪水预报。Flddan的运行情况与监督模型基本使用450-500小时的监督。