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Robust Forecasting of River-Flow Based on Convolutional Neural Network
Huang, Chao1; Zhang, Jing2; Cao, Longpeng2; Wang, Long2; Luo, Xiong2; Wang, Jenq Haur3; Bensoussan, Alain4
Source PublicationIEEE Transactions on Sustainable Computing

In this paper, a novel method is developed for day-ahead daily river-flow forecasting based on convolutional neural network (CNN). The proposed method incorporates both spatial and temporal information to improve the forecasting performance. A CNN model is usually trained by minimizing the mean squared error which is, however, sensitive to few particularly large errors. This character of squared error loss function will result in a poor estimator. To tackle the problem, a robust loss function is proposed to train the CNN. To facilitate the training of CNNs for multiple sites forecasting, transfer learning is also applied in this study. With transfer learning, a new CNN inherits the structure and partial learnable parameters from a well-trained CNN to reduce the training complexity. The forecasting performance of the proposed method is validated with real data of four rivers by comparing with widely used benchmarking models including the autoregressive model, multilayer perception network, kernel ridge regression, radial basis function neural network, and generic CNN. Numerical results show that the proposed method performs best in terms of the root mean squared error, mean absolute error, and mean absolute percentage error. The two-sample Kolmogorov-Smirnov test is further applied to assess the confidence on the conclusion.

KeywordConvolutional Neural Network River-flow Forecasting Robust Loss Function Transfer Learning
URLView the original
Indexed BySCIE
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Information Systems ; Telecommunications
WOS IDWOS:000694028000011
Scopus ID2-s2.0-85082548084
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Cited Times [WOS]:7   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorWang, Long
Affiliation1.State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao
2.School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing, 100083, China
3.Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei City, 106, Taiwan
4.School of Data Science, City University of Hong Kong, Kowloon, Hong Kong
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Huang, Chao,Zhang, Jing,Cao, Longpeng,et al. Robust Forecasting of River-Flow Based on Convolutional Neural Network[J]. IEEE Transactions on Sustainable Computing,2020,5(4):594-600.
APA Huang, Chao,Zhang, Jing,Cao, Longpeng,Wang, Long,Luo, Xiong,Wang, Jenq Haur,&Bensoussan, Alain.(2020).Robust Forecasting of River-Flow Based on Convolutional Neural Network.IEEE Transactions on Sustainable Computing,5(4),594-600.
MLA Huang, Chao,et al."Robust Forecasting of River-Flow Based on Convolutional Neural Network".IEEE Transactions on Sustainable Computing 5.4(2020):594-600.
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