Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks
Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks
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DOI：
10.5194/hess-22-6005-2018
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发表时间：
2018-11-22
影响因子：
6.3
通讯作者：
Herrnegger, Mathew
中科院分区：
文献类型：
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作者：
Kratzert, Frederik;Klotz, Daniel;Herrnegger, Mathew
降雨径流模拟是水文学领域的主要挑战之一。存在各种方法，从基于物理的概念模型到完全数据驱动的模型。在本文中，我们提出了一种新的数据驱动方法，使用长短期记忆（LSTM）网络，一种特殊类型的递归神经网络。LSTM的优势在于它能够学习网络输入和输出之间的长期依赖关系，这对于模拟具有雪影响的集水区的存储效应至关重要。我们使用241集水区的免费提供的CAMELS数据集来测试我们的方法，并将结果与著名的萨克拉门托土壤水分核算模型（SAC-SMA）加上雪-17雪例程。我们还展示了LSTM作为区域水文模型的潜力，其中一个模型预测了各种集水区的流量。在我们的最后一个实验中，我们展示了将在区域规模上学习的过程理解转移到单个集水区的可能性，从而与仅在单个集水区数据上训练的LSTM相比，提高了模型性能。使用这种方法，我们能够实现更好的模型性能，如SAC-SMA + Snow-17，这强调了LSTM在水文建模应用中的潜力。
Rainfall-runoff modelling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data-driven models. In this paper, we propose a novel data-driven approach, using the Long Short-Term Memory (LSTM) network, a special type of recurrent neural network. The advantage of the LSTM is its ability to learn long-term dependencies between the provided input and output of the network, which are essential for modelling storage effects in e.g. catchments with snow influence. We use 241 catchments of the freely available CAMELS data set to test our approach and also compare the results to the well-known Sacramento Soil Moisture Accounting Model (SAC-SMA) coupled with the Snow-17 snow routine. We also show the potential of the LSTM as a regional hydrological model in which one model predicts the discharge for a variety of catchments. In our last experiment, we show the possibility to transfer process understanding, learned at regional scale, to individual catchments and thereby increasing model performance when compared to a LSTM trained only on the data of single catchments. Using this approach, we were able to achieve better model performance as the SAC-SMA + Snow-17, which underlines the potential of the LSTM for hydrological modelling applications.