Streamflow forecasting with deep learning models: A side-by-side comparison in Northwest Spain

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Juan F. Farfán-Durán, Luis Cea
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Abstract

Accurate hourly streamflow prediction is crucial for managing water resources, particularly in smaller basins with short response times. This study evaluates six deep learning (DL) models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and their hybrids (CNN-LSTM, CNN-GRU, CNN-Recurrent Neural Network (RNN)), across two basins in Northwest Spain over a ten-year period. Findings reveal that GRU models excel, achieving Nash-Sutcliffe Efficiency (NSE) scores of approximately 0.96 and 0.98 for the Groba and Anllóns catchments, respectively, at 1-hour lead times. Hybrid models did not enhance performance, which declines at longer lead times due to basin-specific characteristics such as area and slope, particularly in smaller basins where NSE dropped from 0.969 to 0.24. The inclusion of future rainfall data in the input sequences has improved the results, especially for longer lead times from 0.24 to 0.70 in the Groba basin and from 0.81 to 0.92 in the Anllóns basin for a 12-hour lead time. This research provides a foundation for future exploration of DL in streamflow forecasting, in which other data sources and model structures can be utilized.

Abstract Image

利用深度学习模型进行水流预报:西班牙西北部的并行比较
精确的每小时流量预测对于水资源管理至关重要,尤其是在响应时间较短的小型流域。本研究评估了六个深度学习(DL)模型,包括长短期记忆(LSTM)、门控递归单元(GRU)、卷积神经网络(CNN)及其混合模型(CNN-LSTM、CNN-GRU、CNN-递归神经网络(RNN))。研究结果表明,GRU 模型表现出色,在 1 小时准备时间内,Groba 和 Anllóns 流域的纳什-萨特克利夫效率(NSE)分别达到约 0.96 和 0.98。混合模型并没有提高效率,由于流域面积和坡度等流域特性,混合模型的效率在更长的准备时间内会下降,特别是在较小的流域,NSE 从 0.969 降至 0.24。在输入序列中加入未来降雨量数据后,结果有所改善,尤其是在较长的前置时间内,格罗巴盆地的结果从 0.24 升至 0.70,安洛盆地的结果从 0.81 升至 0.92(前置时间为 12 小时)。这项研究为今后探索 DL 在河水流量预报中的应用奠定了基础,在此过程中还可以利用其他数据源和模型结构。
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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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