The Applicability of LSTM-KNN Model for Real-Time Flood Forecasting in Different Climate Zones in China




Liu, Moyang
Huang, Yingchun
Li, Zhijia
Tong, Bingxing
Liu, Zhentao
Sun, Mingkun
Jiang, Feiqing
Zhang, Hanchen

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Flow forecasting is an essential topic for flood prevention and mitigation. This study utilizes a data-driven approach, the Long Short-Term Memory neural network (LSTM), to simulate rainfall–runoff relationships for catchments with different climate conditions. The LSTM method presented was tested in three catchments with distinct climate zones in China. The recurrent neural network (RNN) was adopted for comparison to verify the superiority of the LSTM model in terms of time series prediction problems. The results of LSTM were also compared with a widely used process-based model, the Xinanjiang model (XAJ), as a benchmark to test the applicability of this novel method. The results suggest that LSTM could provide comparable quality predictions as the XAJ model and can be considered an efficient hydrology modeling approach. A real-time forecasting approach coupled with the k-nearest neighbor (KNN) algorithm as an updating method was proposed in this study to generalize the plausibility of the LSTM method for flood forecasting in a decision support system. We compared the simulation results of the LSTM and the LSTM-KNN model, which demonstrated the effectiveness of the LSTM-KNN model in the study areas and underscored the potential of the proposed model for real-time flood forecasting.



data-driven model, LSTM, Xinanjiang model, KNN, real-time hydrological forecasting


Water 12 (2): 440 (2020)


Electrical and Computer Engineering