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DA-Net: Dual Attention Network for Flood Forecasting

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Abstract

Flood is difficult to predict due to its extreme runoff values, short duration and complex generation mechanism. In order to reduce the negative effects of flood disasters, researchers try to forecast flood by utilizing deep learning technology. Essentially, historical flood data can be regarded as sequential data with sets of flood factors. Facing challenges brought by inherent characteristics of flood forecasting, this paper proposes a dual attention embedding network, i.e., DA-Net, to achieve accurate prediction results. The proposed attention mechanism not only embeds a convolution self-attention module (CSA) on Temporal Convolutional Network (TCN) for description of local context information, but also constructs a Temporal-related Feature Attention (TFA) Module to assign time-varying weights for different features in a global sense. Specifically, CSA offers additional and local context information to help predict extreme runoff values even within a small period, meanwhile TFA improves global modeling capability of TCN for construction of data-driven generation mechanism in our method. Experiments on Changhua and Tunxi watershed dataset show the proposed method achieves superior prediction performance than current deep learning based methods.

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Data Availability

The datasets generated during and analysed during the current study are not publicly available due to privacy reasons, but are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by National Key R &D Program of China under Grant No. 2021YFB3900601, National Natural Science Foundation of China under Grant No. 62172438, the Fundamental Research Funds for the Central Universities under Grant B220202074, and the Fundamental Research Funds for the Central Universities, JLU.

Funding

Laboratory of AI and Information Processing (Hechi University), Education Department of Guangxi Zhuang Autonomous Region under Grant 2022GXZDSY014, and Research Funding Project of Jiangsu Hydraulic Research Institute No. 2022Z028.

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Authors and Affiliations

  1. Jiangsu Hydraulic Research Institute, Nanhu Road, Nanjing, 210017, Jiangsu, China

    Qian Cheng

  2. Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Fochengxi Street, Nanjing, 210096, Jiangsu, China

    Yirui Wu

  3. College of Computer and Information, Hohai University, Fochengxi Street, Nanjing, 210096, Jiangsu, China

    Yirui Wu

  4. Department of Science and Technology, University of Naples Parthenope, Naples, Italy

    Aniello Castiglione

  5. Department of Computer Science, University of Salerno, Fisciano, Italy

    Fabio Narducci

  6. Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, 518110, Guangdong, Shenzhen, China

    Shaohua Wan

  7. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Qianjin Road, Changchun, 130015, Jilin, China

    Yirui Wu

  8. Key Laboratory of AI and Information Processing, Hechi University, Guangxi, 546300, Yizhou, China

    Shaohua Wan

Authors
  1. Qian Cheng

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  2. Yirui Wu

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  3. Aniello Castiglione

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  4. Fabio Narducci

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  5. Shaohua Wan

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Correspondence toShaohua Wan.

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Cheng, Q., Wu, Y., Castiglione, A.et al. DA-Net: Dual Attention Network for Flood Forecasting.J Sign Process Syst95, 351–362 (2023). https://doi.org/10.1007/s11265-023-01839-x

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