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Open AccessEditor’s ChoiceArticle

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

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College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
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School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, USA
3
Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA
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Institute of Environmental Engineering, Ningxia University, Yinchuan 750021, China
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Ningxia Key Laboratory of Resource Assessment and Environment Regulation in Arid Region, Yinchuan 750021, China
6
China-Arab Joint International Research Laboratory for Featured Resources and Environmental Governance in Arid Region, Yinchuan 750021, China
*
Author to whom correspondence should be addressed.
Water 2020, 12(2), 440; https://doi.org/10.3390/w12020440
Received: 19 November 2019 / Revised: 12 January 2020 / Accepted: 3 February 2020 / Published: 6 February 2020
(This article belongs to the Section Hydrology and Hydrogeology)
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. View Full-Text
Keywords: data-driven model; LSTM; Xinanjiang model; KNN; real-time hydrological forecasting data-driven model; LSTM; Xinanjiang model; KNN; real-time hydrological forecasting
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Liu, M.; Huang, Y.; Li, Z.; Tong, B.; Liu, Z.; Sun, M.; Jiang, F.; Zhang, H. The Applicability of LSTM-KNN Model for Real-Time Flood Forecasting in Different Climate Zones in China. Water 2020, 12, 440.

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