Leveraging LSTM Embeddings for River Water Temperature Modeling

River water temperature modeling is a major task in climate research. State-of-the-art methods for water temperature modeling deploy a transductive design, which makes it difficult to generalize to unseen water stations during test time. In the present paper, we isolate one of the common building blocks – a central LSTM, trained for each water station – and propose an embedding scheme in order to increase both the prediction accuracy and the amount of shared parameters and thus the generalization. The proposed embeddings are learned during training time. In an empirical evaluation we show that our method is able to reduce the RMSE by about five percentage points compared to the state-of-the-art reference method while decreasing the tuneable parameters by several orders of magnitude. We also provide a sample analysis of the embedding space of the catchment area of one specific river. Looking at the results of this qualitative analysis, we come to the conclusion that deploying an embedding in water temperature models is not only convincing to decrease the RMSE of water temperature predictions, but also enables better explainable deep learning models. Moreover, the proposed embedding technique opens up various unexplored applications in water temperature research.