Meta-Transfer Learning: An application to Streamflow modeling in River-streams
Meta-Transfer Learning: An application to Streamflow modeling in River-streams
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DOI：
10.1109/icdm54844.2022.00026
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Rahul Ghosh;Bangyan Li;Kshitij Tayal;Vipin Kumar;X. Jia
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文献类型：
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作者：
Rahul Ghosh;Bangyan Li;Kshitij Tayal;Vipin Kumar;X. Jia
非监测实体对输入驱动的响应预测已被公认为许多科学问题中最重要的问题之一。这个问题是具有挑战性的，因为在空间和时间上数据观测的动态基础上的非平稳过程。因此，由于数据分布的变化，直接将模型从观察良好的数据实体转移到未监测的目标实体，往往会导致性能次优。本文提出了一种新的元迁移学习框架，该框架可以自动估计实体之间的相似性，从而将知识从良好观察的实体转移到未监测的实体。序列自编码器嵌入时间序列数据的时间行为和传统的基于物理的模型产生的模拟。该嵌入模型在源到源迁移经验的指导下，在元迁移学习框架中进行训练。我们在特拉华河流域的多个河段的流量预测中测试了这种方法，特拉华河流域是美国东海岸一个生态多样化的地区。实验结果表明，与多种基线相比，该方法在预测未监测河段的流量方面具有优越性。我们的方法还在片段之间创建了有意义的相似性估计，以指导迁移学习过程。
Prediction of response to input drivers by unmonitored entities has been recognized as one of the most important problems in many scientific problems. This problem is challenging due to the non-stationary processes that underlie the dynamics of data observations over space and time. Hence, directly transferring models from well-observed data entities to unmonitored target entity often lead to sub-optimal performance due to the shift in data distribution. This paper proposes a new meta-transfer learning framework that automatically estimates the similarity amongst entities to transfer knowledge from well-observed entities to unmonitored entities. A sequence autoencoder embeds temporal behaviors of time series data and simulations generated by traditional physics-based models. This embedding model is trained in a meta-transfer learning framework under the guidance of source-to-source transferring experiences. We tested this method in streamflow prediction for multiple river segments in the Delaware River Basin, an ecologically diverse region along the eastern coast of the United States. The experimental results demonstrate the superiority of the proposed method in predicting streamflow for unmonitored stream segments compared to a diverse set of baselines. Our method also creates meaningful similarity estimates amongst segments to guide the transfer learning process.