Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation
Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation
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使用长短期记忆网络方法进行降雨径流模拟的深度学习
DOI：
10.3390/w10111543
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发表时间：
2018-11-01
期刊：
影响因子：
3.4
通讯作者：
Lou, Zhengzheng
中科院分区：
文献类型：
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作者：
Hu, Caihong;Wu, Qiang;Lou, Zhengzheng
由于径流过程的高度随机性和非静态性，为了研究这一复杂现象，人们建立了许多模型。最近，机器学习技术，如人工神经网络（ANN）和其他网络已被水文学家广泛用于径流建模以及其他水文领域。然而，深度学习方法，如最先进的LSTM网络，在水文序列时间序列预测中研究得很少。基于汾河流域14个雨量站和1个水文站1971 - 2013年的洪水数据，采用人工神经网络和LSTM网络模型对汾河流域的径流过程进行模拟。试验数据来自98次径流事件。其中86个径流事件作为训练集，其余的作为测试集。结果表明，这两种网络都适用于全流域径流模型，且优于概念模型和物理模型。LSTM模型优于ANN模型，其R2和N S E的值分别超过0.9。考虑到不同的提前期建模，LSTM模型也比ANN模型更稳定，具有更好的仿真性能。遗忘门的特殊单元使LSTM模型比ANN模型更好地模拟和更智能。在这项研究中，我们希望提出新的数据驱动的洪水预报方法。
Considering the high random and non-static property of the rainfall-runoff process, lots of models are being developed in order to learn about such a complex phenomenon. Recently, Machine learning techniques such as the Artificial Neural Network (ANN) and other networks have been extensively used by hydrologists for rainfall-runoff modelling as well as for other fields of hydrology. However, deep learning methods such as the state-of-the-art for LSTM networks are little studied in hydrological sequence time-series predictions. We deployed ANN and LSTM network models for simulating the rainfall-runoff process based on flood events from 1971 to 2013 in Fen River basin monitored through 14 rainfall stations and one hydrologic station in the catchment. The experimental data were from 98 rainfall-runoff events in this period. In between 86 rainfall-runoff events were used as training set, and the rest were used as test set. The results show that the two networks are all suitable for rainfall-runoff models and better than conceptual and physical based models. LSTM models outperform the ANN models with the values of R 2 and N S E beyond 0.9, respectively. Considering different lead time modelling the LSTM model is also more stable than ANN model holding better simulation performance. The special units of forget gate makes LSTM model better simulation and more intelligent than ANN model. In this study, we want to propose new data-driven methods for flood forecasting.