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Real-time Flood Classification Forecasting Based on k-means++ Clustering and Neural Network

Author

Listed:
  • Hu Caihong

    (Zhengzhou University
    Yellow River Institute for Ecological Protection & Regional Coordinated Development)

  • Zhang Xueli

    (Zhengzhou University
    Yellow River Institute for Ecological Protection & Regional Coordinated Development)

  • Li Changqing

    (Shangdong survey and design institute of water conservancy)

  • Liu Chengshuai

    (Zhengzhou University
    Yellow River Institute for Ecological Protection & Regional Coordinated Development)

  • Wang Jinxing

    (Information Center of Ministry of Water Resources)

  • Jian Shengqi

    (Zhengzhou University
    State Key Laboratory of Soil Erision and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation)

Abstract

Floods are among the most dangerous disasters that affect human beings. Timely and accurate flood forecasting can effectively reduce losses to human life and property and improve the utilization of flood resources. In this study, a real-time flood classification and prediction method (RFC-P) was constructed based on factor analysis, the k-means++ clustering algorithm, SSE, a backpropagation neural network (BPNN) and the M-EIES model. Model parameters of different flood types were obtained to forecast floods. The RFC-P method was applied to the Jingle sub-basin in Shanxi Province. The results showed that the RFC-P method can be used for the real-time classification and prediction of floods. The parameters of the flood classification and prediction model were consistent with the characteristics of the flood events. Compared with the results of unclassified predictions, the Nash coefficient increased by 5%–11.62%, the relative error of the average flood peak was reduced by 6.08%–12.7%, the relative error of the average flood volume was reduced by 5.74%–8.07%, and the time difference of the average peak was reduced by 43%–66% based on the proposed approach. The methodology proposed in this study can be used to identify extreme flood events and provide scientific support for flood classification and prediction, flood control and disaster reduction in river basins, and the efficient utilization of water resources.

Suggested Citation

  • Hu Caihong & Zhang Xueli & Li Changqing & Liu Chengshuai & Wang Jinxing & Jian Shengqi, 2022. "Real-time Flood Classification Forecasting Based on k-means++ Clustering and Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 103-117, January.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:1:d:10.1007_s11269-021-03014-y
    DOI: 10.1007/s11269-021-03014-y
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    References listed on IDEAS

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    1. Xinyu Wan & Qingyan Yang & Peng Jiang & Ping’an Zhong, 2019. "A Hybrid Model for Real-Time Probabilistic Flood Forecasting Using Elman Neural Network with Heterogeneity of Error Distributions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 4027-4050, September.
    2. William Meredith, 1993. "Measurement invariance, factor analysis and factorial invariance," Psychometrika, Springer;The Psychometric Society, vol. 58(4), pages 525-543, December.
    3. Zening Wu & Bingyan Ma & Huiliang Wang & Caihong Hu & Hong Lv & Xiangyang Zhang, 2021. "Identification of Sensitive Parameters of Urban Flood Model Based on Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(7), pages 2115-2128, May.
    4. T. Reshma & K. Venkata Reddy & Deva Pratap & V. Agilan, 2018. "Parameters Optimization using Fuzzy Rule Based Multi-Objective Genetic Algorithm for an Event Based Rainfall-Runoff Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1501-1516, March.
    5. Peiman Parisouj & Hamid Mohebzadeh & Taesam Lee, 2020. "Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4113-4131, October.
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