Probing the limit of hydrologic predictability with the Transformer network

• URL: http://arxiv.org/abs/2306.12384v1
• Date: Wed, 21 Jun 2023 17:06:54 GMT
• Title: Probing the limit of hydrologic predictability with the Transformer network
• Authors: Jiangtao Liu, Yuchen Bian and Chaopeng Shen
• Abstract summary: We show that a vanilla Transformer architecture is not competitive against LSTM on the widely benchmarked CAMELS dataset. A recurrence-free variant of Transformer can obtain mixed comparisons with LSTM, producing the same Kling-Gupta efficiency coefficient (KGE) along with other metrics. While the Transformer results are not higher than current state-of-the-art, we still learned some valuable lessons.
• Score: 7.326504492614808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For a number of years since its introduction to hydrology, recurrent neural networks like long short-term memory (LSTM) have proven remarkably difficult to surpass in terms of daily hydrograph metrics on known, comparable benchmarks. Outside of hydrology, Transformers have now become the model of choice for sequential prediction tasks, making it a curious architecture to investigate. Here, we first show that a vanilla Transformer architecture is not competitive against LSTM on the widely benchmarked CAMELS dataset, and lagged especially for the high-flow metrics due to short-term processes. However, a recurrence-free variant of Transformer can obtain mixed comparisons with LSTM, producing the same Kling-Gupta efficiency coefficient (KGE), along with other metrics. The lack of advantages for the Transformer is linked to the Markovian nature of the hydrologic prediction problem. Similar to LSTM, the Transformer can also merge multiple forcing dataset to improve model performance. While the Transformer results are not higher than current state-of-the-art, we still learned some valuable lessons: (1) the vanilla Transformer architecture is not suitable for hydrologic modeling; (2) the proposed recurrence-free modification can improve Transformer performance so future work can continue to test more of such modifications; and (3) the prediction limits on the dataset should be close to the current state-of-the-art model. As a non-recurrent model, the Transformer may bear scale advantages for learning from bigger datasets and storing knowledge. This work serves as a reference point for future modifications of the model.

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