How to improve the performance of runoff predictions in ungauged basins (PUB) is challenging. Recently, the long short-term memory (LSTM) based models have excellent performance and receive many attentions. In this paper, to improve the performance for 1-day-ahead runoff PUB and provide good performance for multi-day-ahead runoff PUB, we propose four sequence-to-sequence (S2S) models to deal with PUB. Furthermore, we introduce two modules named attribute-weighting module and multi-head-attention module for improving the performance. To show the power of these S2S models and the advantages of those two modules, we test our four S2S models and also three benchmark models including a PUB-LSTM model and two process-driven models by using k-fold validation on 531 basins of the Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset. For 1-day-ahead runoff PUB, the median and the mean of Nash–Sutcliffe efficiency for the 531 basins provided by our best model achieve $0.78$ and $0.70$, respectively, while those provided by the PUB-LSTM model (the best one among those three benchmark models) are $0.69$ and $0.54$, respectively. Besides, our S2S models have promising results for multi-day-ahead runoff PUB. For $7$th-day-ahead runoff PUB, the median and the mean of Nash–Sutcliffe efficiency for the 531 basins provided by our best model are $0.68$ and $0.57$, respectively. Furthermore, the results also show that the two modules are beneficial for improving the performance.

如何提高未测量盆地 (PUB) 中径流预测的性能具有挑战性。最近，基于长短期记忆（LSTM）的模型具有优异的性能并受到很多关注。在本文中，为了提高 1 天前径流 PUB 的性能并为多天前径流 PUB 提供良好的性能，我们提出了四种序列到序列（S2S）模型来处理 PUB。此外，我们引入了两个名为属性权重模块和多头注意力模块的模块来提高性能。为了展示这些 S2S 模型的强大功能以及这两个模块的优势，我们测试了我们的四个 S2S 模型和三个基准模型，包括一个 PUB-LSTM 模型和两个过程驱动模型，方法是在大样本研究 (CAMELS) 数据集的集水属性和气象学 (CAMELS) 数据集的 531 个流域上使用 k 折验证。对于 1 天前径流 PUB，我们的最佳模型提供的 531 个盆地的 Nash-Sutcliffe 效率的中值和平均值达到$0.78$ 和 $0.70$, 而那些由 PUB-LSTM 模型（这三个基准模型中最好的模型）提供的 $0.69$ 和 $0.54$， 分别。此外，我们的 S2S 模型对于多日前径流 PUB 具有可喜的结果。为了$7$前 th 天径流 PUB，我们的最佳模型提供的 531 个盆地的 Nash-Sutcliffe 效率的中位数和平均值为 $0.68$ 和 $0.57$， 分别。此外，结果还表明，这两个模块有利于提高性能。