Abstract
Many hydrological applications related to water resource planning and management primarily rely on a succession of streamflow forecasts with extensive lead times. In this study, two innovative models, termed as DirCNN and DRCNN, are proposed for multi-step-ahead (MSA) monthly streamflow forecasting based on the direct (Dir) and direct-recursive (DR) strategies and using the convolutional neural network (CNN) to automatically extract input variables. Compared to traditional MSA forecasting models, DirCNN and DRCNN can automatically extract input variables and predict streamflow for multiple lead times simultaneously. Xiangjiaba Hydropower Station, Huanren Reservoir, and Fengman Reservoir in China were included as case studies, and three artificial neural networks based models are used as comparative models. The most important results are highlighted below. First, the proposed DirCNN and DRCNN exhibit comparable prediction performances but outperform the comparison models. Second, with the increase in lead time, DirCNN and DRCNN demonstrate good consistency in forecasting accuracy. Third, the stacking order of candidate sequences has little effect on the DirCNN and DRCNN forecasting accuracy. These results suggest that DirCNN and DRCNN could be ahead of MSA monthly streamflow forecasting and thus would be helpful in the judicious use of water resources.
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Funding
This work was supported by the National Natural Science Foundation of China [U2240204] and the fund of Innovation research team from the department of science and technology in Liaoning Province [XLYC1908023].
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Yong Peng designed the study. Xingsheng Shu performed the research and wrote the initial draft of the manuscript. Wei Ding analyzed the data and made revisions to the draft. Ziru Wang contributed to the revisions. Jian Wu contributed to the revisions.
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Highlights
• Two CNN-based models for MSA monthly streamflow forecasting are proposed.
• DirCNN and DRCNN provide satisfactory monthly streamflow forecasting up to 12 months ahead.
• The stacking order of candidate sequences has little effect on the forecasting accuracies of DirCNN and DRCNN.
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Shu, X., Peng, Y., Ding, W. et al. Multi-Step-Ahead Monthly Streamflow Forecasting Using Convolutional Neural Networks. Water Resour Manage 36, 3949–3964 (2022). https://doi.org/10.1007/s11269-022-03165-6
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DOI: https://doi.org/10.1007/s11269-022-03165-6