Prediction of Yangtze River streamflow based on deep learning neural network with El Niño-Southern Oscillation.
Prediction of Yangtze River streamflow based on deep learning neural network with El Niño-Southern Oscillation.
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DOI：
10.1038/s41598-021-90964-3
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发表时间：
2021-06-03
影响因子：
4.6
通讯作者：
Mu L
中科院分区：
文献类型：
--
作者：
Ha S;Liu D;Mu L
长期径流和洪水预报一直是水文学研究的重要方向。当今世界，气候变化、洪水等异常现象的发生越来越频繁，给社会带来了巨大的损失。径流预报，特别是洪水预报，对防灾减灾具有重要意义。现有的基于物理机制的水文模型能较准确地预报径流，但预报有效期仅为1个月左右，不利于决策。人工神经网络（ANN）在径流预测方面具有很大的潜力，它不仅擅长处理非线性数据，而且还可以进行长期预测。然而，大多数人工神经网络模型在面对原始流量数据时预测不稳定，并且在预测极端流量时存在过大的误差。以往的研究表明，厄尔尼诺-南方涛动（ENSO）和长江流量之间的联系。本文利用ENSO和长江1952 - 2018年的月径流量资料，利用深度神经网络对长江两个极端洪水年和一个小洪水年的月径流量进行预测。本文使用了三种深度神经网络框架：堆叠式长短期记忆、Conv长短期记忆编码器-解码器长短期记忆和Conv长短期记忆编码器-解码器门递归单元。结果表明，使用ConvLSTM提高了模型的稳定性，提高了洪水预报的精度。此外，ENSO的引入，导致更准确地预测洪峰和洪水的发生时间的实验数据。此外，在卷积长短期记忆+编解码器门递归单元模型上获得了最好的结果。
Accurate long-term streamflow and flood forecasting have always been an important research direction in hydrology research. Nowadays, climate change, floods, and other anomalies occurring more and more frequently and bringing great losses to society. The prediction of streamflow, especially flood prediction, is important for disaster prevention. Current hydrological models based on physical mechanisms can give accurate predictions of streamflow, but the effective prediction period is only about 1 month in advance, which is too short for decision making. The artificial neural network (ANN) has great potential for predicting runoff and is not only good at handling non-linear data but can also make long-period forecasts. However, most of ANN models are unstable in their predictions when faced with raw flow data, and have excessive errors in predicting extreme flows. Previous studies have shown a link between the El Niño–Southern Oscillation (ENSO) and the streamflow of the Yangtze River. In this paper, we use ENSO and the monthly streamflow data of the Yangtze River from 1952 to 2018 to predict the monthly streamflow of the Yangtze River in two extreme flood years and a small flood year by using deep neural networks. In this paper, three deep neural network frameworks are used: stacked long short-term memory, Conv long short-term memory encoder–decoder long short-term memory and Conv long short-term memory encoder–decoder gate recurrent unit. The results show that the use of ConvLSTM improves the stability of the model and increases the accuracy of the flood prediction. Besides, the introduction of ENSO to the experimental data resulted in a more accurate prediction of the time of the occurrence of flood peaks and flood flows. Furthermore, the best results were obtained on the convolutional long short-term memory + encoder–decoder gate recurrent unit model.
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