Articles | Volume 27, issue 9
https://doi.org/10.5194/hess-27-1791-2023
https://doi.org/10.5194/hess-27-1791-2023
Research article
 | 
05 May 2023
Research article |  | 05 May 2023

A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions

Qianqian Zhou, Shuai Teng, Zuxiang Situ, Xiaoting Liao, Junman Feng, Gongfa Chen, Jianliang Zhang, and Zonglei Lu

Related subject area

Subject: Urban Hydrology | Techniques and Approaches: Modelling approaches
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Cited articles

Arnone, E., Pumo, D., Francipane, A., La Loggia, G., and Noto, L. V.: The role of urban growth, climate change, and their interplay in altering runoff extremes, Hydrol. Process., 32, 1755–1770, 2018. 
Ashley, R., Garvin, S., Pasche, E., Vassilopoulos, A., and Zevenbergen, C.: Advances in Urban Flood Management, CRC Press, ISBN 978-0367389512, 2007. 
Berggren, K., Packman, J., Ashley, R., and Viklander, M.: Climate changed rainfalls for urban drainage capacity assessment, Urban Water J., 11, 543–556, 2014. 
Berkhahn, S., Fuchs, L., and Neuweiler, I.: An ensemble neural network model for real-time prediction of urban floods, J. Hydrol, 575, 743–754, 2019. 
Ciechulski, T. and Osowski, S.: High Precision LSTM Model for Short-Time Load Forecasting in Power Systems, Energies, 14, 2983, https://doi.org/10.3390/en14112983, 2021. 
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Short summary
A deep-learning-based data-driven model for flood predictions in temporal and spatial dimensions, with the integration of a long short-term memory network, Bayesian optimization, and transfer learning is proposed. The model accurately predicts water depths and flood time series/dynamics for hyetograph inputs, with substantial improvements in computational time. With transfer learning, the model was well applied to a new case study and showed robust compatibility and generalization ability.
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