Monthly Streamflow Forecasting Using Convolutional Neural Network

Xingsheng Shu, Wei Ding (), Yong Peng (), Ziru Wang, Jian Wu and Min Li
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Xingsheng Shu: Dalian University of Technology
Wei Ding: Dalian University of Technology
Yong Peng: Dalian University of Technology
Ziru Wang: Dalian University of Technology
Jian Wu: Dalian University of Technology
Min Li: Dalian University of Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 15, No 2, 5089-5104

Abstract: Abstract Monthly streamflow forecasting is vital for managing water resources. Recently, numerous studies have explored and evidenced the potential of artificial intelligence (AI) models in hydrological forecasting. In this study, the feasibility of the convolutional neural network (CNN), a deep learning method, is explored for monthly streamflow forecasting. CNN can automatically extract critical features from numerous inputs with its convolution–pooling mechanism, which is a distinct advantage compared with other AI models. Hydrological and large-scale atmospheric circulation variables, including rainfall, streamflow, and atmospheric circulation factors are used to establish models and forecast streamflow for Huanren Reservoir and Xiangjiaba Hydropower Station, China. The artificial neural network (ANN) and extreme learning machine (ELM) with inputs identified based on cross-correlation and mutual information analyses are established for comparative analyses. The performances of these models are assessed with several statistical metrics and graphical evaluation methods. The results show that CNN outperforms ANN and ELM in all statistical measures. Moreover, CNN shows better stability in forecasting accuracy.

Keywords: Discharge prediction; Atmospheric circulation factors; Input variable selection; Data-driven model; Feature extraction (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s11269-021-02961-w

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