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

Hu Caihong, Zhang Xueli, Li Changqing, Liu Chengshuai, Wang Jinxing and Jian Shengqi ()
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Hu Caihong: Zhengzhou University
Zhang Xueli: Zhengzhou University
Li Changqing: Shangdong survey and design institute of water conservancy
Liu Chengshuai: Zhengzhou University
Wang Jinxing: Information Center of Ministry of Water Resources
Jian Shengqi: Zhengzhou University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 1, No 6, 103-117

Abstract: 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.

Keywords: Flood classification forecasting; Real-time classification; k-means++; BPNN (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-021-03014-y

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