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

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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.

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Data Availability Statement

The data presented in this study is available on request from the corresponding author.

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Acknowledgements

This project was supported by National key research priorities program of China (2019YFC1510703), National Natural Science Foundation of China (51979250), Key projects of National Natural Science Foundation of China (51739009), Natural Science Foundation of Henan Province (212300410413), Young Talents Promotion Project of Henan Province (2021HYTP030), State Key Laboratory of Soil Erision and Dryland Farming on the Loess Plateau Foundation (A314021402-2021011).

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Authors and Affiliations

  1. College of Water Conservancy Science & Engineering, Zhengzhou University, Zhengzhou, 450001, China

    Hu Caihong, Zhang Xueli, Liu Chengshuai & Jian Shengqi

  2. Yellow River Institute for Ecological Protection & Regional Coordinated Development, Zhengzhou, 450001, China

    Hu Caihong, Zhang Xueli & Liu Chengshuai

  3. Shangdong survey and design institute of water conservancy, Jinan, 250013, China

    Li Changqing

  4. Information Center of Ministry of Water Resources, Beijing, 100032, China

    Wang Jinxing

  5. State Key Laboratory of Soil Erision and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, CAS & MWR, Yangling, 712100, China

    Jian Shengqi

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  1. Hu Caihong
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  2. Zhang Xueli
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  4. Liu Chengshuai
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Correspondence to Jian Shengqi.

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We also thank my other colleagues for their valuable comments and suggestions that have helped improve the manuscript.

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Caihong, H., Xueli, Z., Changqing, L. et al. Real-time Flood Classification Forecasting Based on k-means++ Clustering and Neural Network. Water Resour Manage 36, 103–117 (2022). https://doi.org/10.1007/s11269-021-03014-y

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