基于数据驱动和回波状态网络的溃坝洪水波浪传播特性预测

IF 2.2 3区 工程技术 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Hydroinformatics Pub Date : 2023-11-02 DOI:10.2166/hydro.2023.035
Changli Li, Zheng Han, Yange Li, Ming Li, Weidong Wang, Ningsheng Chen, Guisheng Hu
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引用次数: 0

摘要

摘要溃坝洪水波浪传播的计算预测是水动力学和水文学领域一个长期存在的问题。研究表明,水库计算回声状态网络(RC-ESN)经过少量数据的良好训练,可以准确预测一维溃坝洪水的长期动态行为。采用Lax-Wendroff数值格式求解一维溃坝洪水情景的de Saint-Venant方程,并训练RC-ESN模型。结果表明,RC-ESN模型具有较好的预测能力,可以提前286个时间步预测波的传播行为,且均方根误差小于0.01,优于仅提前81个时间步的传统长短期记忆(LSTM)模型。本文还对RC-ESN的训练集大小、储层大小、谱半径等关键参数的预测精度进行了敏感性分析。结果表明,RC-ESN对训练集大小的依赖较小,1,200-2,600的中等库大小就足够了。我们确认谱半径对预测精度有复杂的影响,目前推荐较小的谱半径。即使改变溃坝初始流深,RC-ESN的预测范围仍大于LSTM的预测范围。
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Data-driven and echo state network-based prediction of wave propagation behavior in dam-break flood
Abstract The computational prediction of wave propagation in dam-break floods is a long-standing problem in hydrodynamics and hydrology. We show that a reservoir computing echo state network (RC-ESN) that is well-trained on a minimal amount of data can accurately predict the long-term dynamic behavior of a one-dimensional dam-break flood. We solve the de Saint-Venant equations for a one-dimensional dam-break flood scenario using the Lax–Wendroff numerical scheme and train the RC-ESN model. The results demonstrate that the RC-ESN model has good prediction ability, as it predicts wave propagation behavior 286 time-steps ahead with a root mean square error smaller than 0.01, outperforming the conventional long short-term memory (LSTM) model, which only predicts 81 time-steps ahead. We also provide a sensitivity analysis of prediction accuracy for RC-ESN's key parameters such as training set size, reservoir size, and spectral radius. Results indicate that the RC-ESN is less dependent on training set size, with a medium reservoir size of 1,200–2,600 sufficient. We confirm that the spectral radius has a complex influence on the prediction accuracy and currently recommend a smaller spectral radius. Even when the initial flow depth of the dam break is changed, the prediction horizon of RC-ESN remains greater than that of LSTM.
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来源期刊
Journal of Hydroinformatics
Journal of Hydroinformatics 工程技术-工程:土木
CiteScore
4.80
自引率
3.70%
发文量
59
审稿时长
3 months
期刊介绍: Journal of Hydroinformatics is a peer-reviewed journal devoted to the application of information technology in the widest sense to problems of the aquatic environment. It promotes Hydroinformatics as a cross-disciplinary field of study, combining technological, human-sociological and more general environmental interests, including an ethical perspective.
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