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Multi-Step-Ahead Monthly Streamflow Forecasting Using Convolutional Neural Networks by Xingsheng Shu; Yong Peng; Wei Ding; Ziru Wang; Jian Wu is a Environmental Science article available to read on EtoBox.

What is Multi-Step-Ahead Monthly Streamflow Forecasting Using Convolutional Neural Networks about?

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

Who reads Multi-Step-Ahead Monthly Streamflow Forecasting Using Convolutional Neural Networks?

Author

Xingsheng Shu; Yong Peng; Wei Ding; Ziru Wang; Jian Wu

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Environmental Science (Physical Sciences)