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Data-Driven Fast Real-Time Flood Forecasting Model for Processing Concept Drift

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Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications (CloudComp 2019, SmartGift 2019)

Abstract

The hydrological data of small and medium watershed develops with the passage of time. The rainfall-runoff patterns in these data often develop over time, and the models established for the analysis of such data will soon not be applicable. In view of the problem that adaptability and accuracy of the existing data-driven flood real-time forecasting model in medium and small watershed with concept drift. We update the data-driven model using incremental training based on support vector machine (SVM) and gated recurrent unit (GRU) model respectively. According to the rapid real-time flood forecasting test results of the Tunxi watershed, Anhui Province, China, the fast real-time flood forecast data-driven model with incremental update can more accurately predict the moment when the flood begins to rise and the highest point of flood stream-flow, and it is an effective tool for real-time flood forecasting in small and medium watersheds.

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Author information

Authors and Affiliations

  1. Computer and Information College, Hohai University, Nanjing, 211100, China

    Le Yan, Jun Feng, Yirui Wu & Tingting Hang

  2. National Key Lab for Novel Software Technology, Nanjing University, Nanjing, China

    Yirui Wu

  3. Key Laboratory of Unmanned Aerial Vehicle Development & Data Application of Anhui Higher Education Institutes, Wanjiang University of Technology, Maanshan, 243031, China

    Tingting Hang

Authors
  1. Le Yan
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  2. Jun Feng
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  3. Yirui Wu
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  4. Tingting Hang
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Correspondence to Le Yan .

Editor information

Editors and Affiliations

  1. Macquarie University, Sydney, NSW, Australia

    Xuyun Zhang

  2. Macquarie University, Sydney, NSW, Australia

    Guanfeng Liu

  3. Pace University, New York, NY, USA

    Meikang Qiu

  4. James Cook University, Cairns, QLD, Australia

    Wei Xiang

  5. James Cook University, Smithfield, QLD, Australia

    Tao Huang

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Yan, L., Feng, J., Wu, Y., Hang, T. (2020). Data-Driven Fast Real-Time Flood Forecasting Model for Processing Concept Drift. In: Zhang, X., Liu, G., Qiu, M., Xiang, W., Huang, T. (eds) Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. CloudComp SmartGift 2019 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-030-48513-9_30

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  • DOI: https://doi.org/10.1007/978-3-030-48513-9_30

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